LopezIbanez.bib

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@preamble{{\providecommand{\MaxMinAntSystem}{{$\cal MAX$--$\cal MIN$} {A}nt {S}ystem} } # {\providecommand{\Rpackage}[1]{#1} } # {\providecommand{\SoftwarePackage}[1]{#1} } # {\providecommand{\proglang}[1]{#1} }}
@techreport{BezLopStu2014:automoeaTR,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Com\-ponent-Wise Design of Multi-Objective
                  Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  number = {TR/IRIDIA/2014-012},
  address = {Brussels, Belgium},
  month = aug
}
@techreport{IRIDIA-2014-014,
  author = { Vito Trianni  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Advantages of Multi-Objective Optimisation in Evolutionary
                  Robotics: Survey and Case Studies},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  number = {TR/IRIDIA/2014-014},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2014-014.pdf}
}
@techreport{IRIDIA-2014-009,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
  title = {Local Optimal Sets and Bounded Archiving on
                  Multi-objective {NK}-Landscapes with Correlated
                  Objectives},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2014,
  number = {TR/IRIDIA/2014-009}
}
@techreport{IRIDIA-2013-015,
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle },
  year = 2013,
  title = {Grammar-based generation of stochastic local search
                  heuristics through automatic algorithm configuration
                  tools},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2013-015}
}
@techreport{IRIDIA-2012-012,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Improving the Anytime Behaviour of
                  Optimisation Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2012,
  number = {TR/IRIDIA/2012-012},
  month = may,
  note = {Published in European Journal of Operations Research~\cite{LopStu2013ejor}}
}
@techreport{IRIDIA-2012-019,
  author = { Andreea Radulescu  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Improving the Anytime Behaviour of
                  Multiobjective Evolutionary Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2012,
  number = {TR/IRIDIA/2012-019},
  note = {Published in the proceedings of EMO 2013~\cite{RadLopStu2013emo}}
}
@techreport{IRIDIA-2011-003,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The Automatic Design of Multi-Objective Ant Colony
                  Optimization Algorithms},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-003},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-003.pdf},
  note = {Published in IEEE Transactions on Evolutionary
                  Computation~\cite{LopStu2012tec}}
}
@techreport{LopDubStu2011irace,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle  and  Mauro Birattari },
  title = {The {\Rpackage{irace}} package, Iterated Race for
                  Automatic Algorithm Configuration},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-004},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-004.pdf}
}
@techreport{IRIDIA-2011-001,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles  and  Marco Laumanns },
  title = {On Sequential Online Archiving of Objective Vectors},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2011,
  number = {TR/IRIDIA/2011-001},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf},
  note = {This is a revised version of the paper published in EMO 2011~\cite{LopKnoLau2011emo}}
}
@techreport{IRIDIA-2010-002,
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Paola Pellegrini  and  Michael Maur  and  Marco A. {Montes de Oca}  and  Mauro Birattari  and  Marco Dorigo },
  title = {Parameter Adaptation in Ant Colony Optimization},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2010-002},
  year = 2010,
  month = jan,
  note = {Published as a book chapter~\cite{StuLopPel2011autsea}}
}
@techreport{IRIDIA-2009-026,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Adaptive ``Anytime'' Two-Phase Local Search},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2009-026},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-026r001.pdf},
  note = {Published in the proceedings of LION 4~\cite{DubLopStu10:lion-bfsp}}
}
@techreport{IRIDIA-2010-019,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {A Hybrid {TP+PLS} Algorithm for Bi-objective
                  Flow-Shop Scheduling Problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-019},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-019r001.pdf},
  note = {Published in Computers \& Operations Research~\cite{DubLopStu2011cor}}
}
@techreport{IRIDIA-2010-022,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Improving the Anytime Behavior of Two-Phase Local
                  Search},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2010,
  number = {TR/IRIDIA/2010-022},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2010-022r001.pdf},
  note = {Published in Annals of Mathematics and Artificial Intelligence~\cite{DubLopStu2011amai}}
}
@techreport{IRIDIA-2009-015,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2009,
  number = {TR/IRIDIA/2009-015},
  month = may,
  note = {Published as a book chapter~\cite{LopPaqStu09emaa}}
}
@techreport{IRIDIA-2009-019,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Algorithmic Components for
                  Multiobjective Ant Colony Optimization: A Case Study
                  on the Biobjective {TSP}},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2009-019},
  year = 2009,
  month = jun,
  note = {Published in the proceedings of Evolution Artificielle, 2009~\cite{LopStu09ea}}
}
@techreport{IRIDIA-2009-020,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Effective Hybrid Stochastic Local Search Algorithms
                  for Biobjective Permutation Flowshop Scheduling},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  number = {TR/IRIDIA/2009-020},
  year = 2009,
  month = jun,
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2009-020r001.pdf},
  note = {Published in the proceedings of Hybrid Metaheuristics 2009~\cite{DubLopStu09:hm-bfsp}}
}
@techreport{LopBlu08:tsptw,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum },
  title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
                  Study on the {TSP} with Time Windows},
  institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
  year = 2008,
  number = {LSI-08-28},
  note = {Extended version published in Computers \& Operations Research~\cite{LopBlu2010cor}}
}
@techreport{BluBleLop08:lcs,
  author = { Christian Blum  and  Mar{\'\i}a J. Blesa  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Beam Search for the Longest Common Subsequence
                  Problem},
  institution = {Department LSI, Universitat Polit{\`e}cnica de Catalunya},
  year = 2008,
  number = {LSI-08-29},
  note = {Published in Computers \& Operations Research~\cite{BluBleLop09-BeamSearch-LCS}}
}
@techreport{CI-235-07,
  author = { Nicola Beume  and  Carlos M. Fonseca  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Jan Vahrenhold },
  title = {On the Complexity of Computing the Hypervolume
                  Indicator},
  institution = {University of Dortmund},
  year = 2007,
  number = {CI-235/07},
  month = dec,
  note = {Published in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}}
}
@techreport{PaqFonLop06-CSI-klee,
  author = { Lu{\'i}s Paquete  and  Carlos M. Fonseca  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {An optimal algorithm for a special case of {K}lee's
                  measure problem in three dimensions},
  institution = {CSI, Universidade do Algarve},
  year = 2006,
  number = {CSI-RT-I-01/2006},
  abstract = {The measure of the region dominated by (the maxima
                  of) a set of $n$ points in the positive $d$-orthant
                  has been proposed as an indicator of performance in
                  multiobjective optimization, known as the
                  hypervolume indicator, and the problem of computing
                  it efficiently is attracting increasing
                  attention. In this report, this problem is
                  formulated as a special case of Klee's measure
                  problem in $d$ dimensions, which immediately
                  establishes $O(n^{d/2}\log n)$ as a, possibly
                  conservative, upper bound on the required
                  computation time. Then, an $O(n log n)$ algorithm
                  for the 3-dimensional version of this special case
                  is constructed, based on an existing dimension-sweep
                  algorithm for the related maxima problem. Finally,
                  $O(n log n)$ is shown to remain a lower bound on the
                  time required by the hypervolume indicator for
                  $d>1$, which attests the optimality of the algorithm
                  proposed.},
  note = {Superseded by paper in IEEE Transactions on Evolutionary Computation~\cite{BeuFonLopPaqVah09:tec}},
  annote = {Proof of Theorem 3.1 is incorrect}
}
@techreport{PaqStuLop-IRIDIA-2005-029,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {On the design and analysis of {SLS} algorithms for
                  multiobjective combinatorial optimization problems},
  institution = {IRIDIA, Universit{\'e} Libre de Bruxelles, Belgium},
  year = 2005,
  number = {TR/IRIDIA/2005-029},
  abstract = {Effective Stochastic Local Search (SLS) algorithms
                  can be seen as being composed of several algorithmic
                  components, each of which plays some specific role
                  with respect to overall performance. In this
                  article, we explore the application of experimental
                  design techniques to analyze the effect of different
                  choices for these algorithmic components on SLS
                  algorithms applied to Multiobjective Combinatorial
                  Optimization Problems that are solved in terms of
                  {P}areto optimality. This analysis is done using the
                  example application of SLS algorithms to the
                  biobjective Quadratic Assignment Problem and we show
                  also that the same choices for algorithmic
                  components can lead to different behavior in
                  dependence of various instance features, such as the
                  structure of input data and the correlation between
                  objectives.},
  url = {http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2005-029r001.pdf}
}
@techreport{LopPaqStu04:hybrid,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Hybrid Population-based Algorithms for the
                  Bi-objective Quadratic Assignment Problem},
  institution = {FG Intellektik, FB Informatik, TU Darmstadt},
  year = 2004,
  number = {AIDA--04--11},
  month = dec,
  note = {Published in Journal of Mathematical Modelling and Algorithms~\cite{LopPaqStu05:jmma}}
}
@phdthesis{LopezIbanezPhD,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Operational Optimisation of Water Distribution
                  Networks},
  school = {School of Engineering and the Built Environment},
  year = 2009,
  address = {Edinburgh Napier University, UK},
  url = {http://researchrepository.napier.ac.uk/3044/}
}
@phdthesis{LopezDiploma,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Multi-objective Ant Colony Optimization},
  school = {Intellectics Group, Computer Science Department, Technische
                  Universit{\"a}t Darmstadt, Germany},
  year = 2004,
  type = {Diploma thesis},
  pdf = {doc/Lopez-Ibanez_MOACO.pdf}
}
@misc{LopPaqStu2010:eaftools,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {{EAF} Graphical Tools},
  year = 2010,
  url = {http://lopez-ibanez.eu/eaftools},
  annote = {These tools are described in the book chapter
                  ``\emph{Exploratory analysis of stochastic local
                  search algorithms in biobjective
                  optimization}''~\cite{LopPaqStu09emaa}. Please cite
                  the book chapter, not this.}
}
@inproceedings{BezLopStu2015moead,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Comparing De\-com\-po\-sition-Based and Automatically
                  Component-Wise Designed Multi-Objective Evolutionary
                  Algorithms},
  pages = {396--410},
  doi = {10.1007/978-3-319-15934-8_27}
}
@inproceedings{LopPraPae08:WDSA,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Parallel Optimisation Of Pump Schedules With A
                  Thread-Safe Variant Of {EPANET} Toolkit},
  booktitle = {Proceedings of the 10th Annual Water Distribution
                  Systems Analysis Conference (WDSA 2008)},
  year = 2008,
  editor = { Jakobus E. van Zyl  and  A. A. Ilemobade  and  H. E. Jacobs },
  month = aug,
  pdf = {LopezPrasadPaechter-WDSA2008-official.pdf},
  doi = {10.1061/41024(340)40},
  publisher = {ASCE}
}
@inproceedings{LopPraPaech:ccwi2005,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Optimal Pump Scheduling: Representation and Multiple
                  Objectives},
  booktitle = {Proceedings of the Eighth International Conference
                  on Computing and Control for the Water Industry
                  (CCWI 2005)},
  pages = {117--122},
  year = 2005,
  editor = { Dragan A. Savic  and  Godfrey A. Walters  and  Roger King  and  Soon Thiam-Khu },
  volume = 1,
  address = {University of Exeter, UK},
  pdf = {LopPraPae05-ccwi.pdf},
  month = sep
}
@inproceedings{PaqStuLop05mic,
  address = {Vienna, Austria},
  year = 2005,
  booktitle = {6th Metaheuristics International Conference (MIC
                  2005)},
  editor = {Karl F. Doerner and Michel Gendreau and Peter
                  Greistorfer and  Gutjahr, Walter J.  and  Richard F. Hartl and  Marc Reimann },
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Towards the Empirical Analysis of {SLS} Algorithms
                  for Multiobjective Combinatorial Optimization
                  Problems through Experimental Design},
  pages = {739--746},
  abstract = { Stochastic Local Search (SLS) algorithms for
                  Multiobjective Combinatorial Optimization Problems
                  (MCOPs) typically involve the selection and
                  parameterization of many algorithm components whose
                  role with respect to their overall performance and
                  relation to certain instance features is often not
                  clear. In this abstract, we use a modular approach
                  for the design of SLS algorithms for MCOPs defined
                  in terms of {P}areto optimality and we present an
                  extensive analysis of SLS algorithms through
                  experimental design techniques, where each algorithm
                  component is considered a factor. The experimental
                  analysis is based on a sound experimental
                  methodology for analyzing the output of algorithms
                  for MCOPs. We show that different choices for
                  algorithm components can lead to different behavior
                  in dependence of various instance features.},
  pdf = {PaqStuLop05mic.pdf}
}
@incollection{StuLop2015gecco,
  address = {New York, NY},
  publisher = {ACM Press},
  year = 2015,
  editor = {Juan Luis Jim{\'{e}}nez Laredo and Sara Silva and  Anna I. Esparcia{-}Alc{\'{a}}zar },
  booktitle = {{GECCO} (Companion)},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Automatic (Offline) Configuration of Algorithms},
  pages = {681--702},
  doi = {10.1145/2739482.2756581}
}
@incollection{LopKno2015emo,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and  Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9019,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {II}},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles },
  title = {Machine Decision Makers as a Laboratory for Interactive EMO},
  pages = {295--309},
  abstract = {A key challenge, perhaps the central challenge, of
                  multi-objective optimization is how to deal with candidate
                  solutions that are ultimately evaluated by the hidden or
                  unknown preferences of a human decision maker (DM) who
                  understands and cares about the optimization problem.
                  Alternative ways of addressing this challenge exist but
                  perhaps the favoured one currently is the interactive
                  approach (proposed in various forms). Here, an evolutionary
                  multi-objective optimization algorithm (EMOA) is controlled
                  by a series of interactions with the DM so that preferences
                  can be elicited and the direction of search controlled. MCDM
                  has a key role to play in designing and evaluating these
                  approaches, particularly in testing them with real DMs, but
                  so far quantitative assessment of interactive EMOAs has been
                  limited.  In this paper, we propose a conceptual framework
                  for this problem of quantitative assessment, based on the
                  definition of machine decision makers (machine DMs), made
                  somewhat realistic by the incorporation of various
                  non-idealities. The machine DM proposed here draws from
                  earlier models of DM biases and inconsistencies in the MCDM
                  literature.  As a practical illustration of our approach, we
                  use the proposed machine DM to study the performance of an
                  interactive EMOA, and discuss how this framework could help
                  in the evaluation and development of better interactive
                  EMOAs.},
  doi = {10.1007/978-3-319-15892-1_20},
  pdf = {LopKno2015emo.pdf}
}
@incollection{BezLopStu2015emode,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {To {DE} or Not to {DE}? Multi-objective Differential
                  Evolution Revisited from a Component-Wise Perspective},
  pages = {48--63},
  doi = {10.1007/978-3-319-15934-8_4}
}
@incollection{BraCorGre2015dagstuhl,
  keywords = {multiple criteria decision making, evolutionary
                  multiobjective optimization},
  doi = {10.4230/DagRep.5.1.96},
  volume = {5(1)},
  year = 2015,
  series = {Dagstuhl Reports},
  publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
                  Germany},
  booktitle = {Understanding Complexity in Multiobjective Optimization
                  (Dagstuhl Seminar 15031)},
  editor = { Salvatore Greco  and  Kathrin Klamroth  and  Joshua D. Knowles  and  G{\"u}nther Rudolph },
  author = { J{\"u}rgen Branke  and  Salvatore Corrente  and  Salvatore Greco  and  Milosz Kadzinski  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Vincent Mousseau  and  Mauro Munerato  and  Roman S{\l}owi{\'n}ski },
  title = {Behavior-Realistic Artificial Decision-Makers to Test
                  Preference-Based Multi-objective Optimization Method
                  ({W}orking {G}roup ``{M}achine {D}ecision-{M}aking'')},
  pages = {110--116}
}
@incollection{PerLopStu2014ants,
  volume = 8667,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2014,
  booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2014},
  author = {Leslie {P{\'e}rez C{\'a}ceres}  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Ant Colony Optimization on a Budget of 1000},
  doi = {10.1007/978-3-319-09952-1_5},
  pages = {50--61}
}
@incollection{PerLopStu2014evocop,
  publisher = {Springer},
  volume = 8600,
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Proceedings of EvoCOP 2014 -- 14th European Conference on Evolutionary Computation in Combinatorial Optimization},
  author = {Leslie {P{\'e}rez C{\'a}ceres}  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Parameters of irace},
  doi = {10.1007/978-3-662-44320-0_4},
  pages = {37--48}
}
@incollection{LopLieVer2014ppsn,
  year = 2014,
  volume = 8672,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = {Thomas Bartz-Beielstein and J\"{u}rgen Branke and
                  Bogdan Filipi\v{c} and Jim Smith},
  booktitle = {PPSN 2014},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Arnaud Liefooghe  and  Verel, S{\'e}bastien },
  doi = {10.1007/978-3-319-10762-2_61},
  title = {Local Optimal Sets and Bounded Archiving on
                  Multi-objective {NK}-Landscapes with Correlated
                  Objectives},
  pages = {621--630}
}
@incollection{BezLopStu2014:lion,
  publisher = {Springer},
  volume = 8426,
  editor = {Panos M. Pardalos and Mauricio G. C. Resende and
                  Chrysafis Vogiatzis and Jose L. Walteros},
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Deconstructing Multi-Objective Evolutionary
                  Algorithms: An Iterative Analysis on the Permutation
                  Flowshop},
  pages = {57--172},
  doi = {10.1007/978-3-319-09584-4_16},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-010/}
}
@incollection{BezLopStu2014:ppsn,
  year = 2014,
  volume = 8672,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = {Thomas Bartz-Beielstein and J\"{u}rgen Branke and
                  Bogdan Filipi\v{c} and Jim Smith},
  booktitle = {PPSN 2014},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Evolutionary Algorithms for
                  Multi-Objective Combinatorial Optimization},
  doi = {10.1007/978-3-319-10762-2_50},
  pages = {508--517}
}
@incollection{HutLopFaw2014lion,
  publisher = {Springer},
  volume = 8426,
  editor = {Panos M. Pardalos and Mauricio G. C. Resende and
                  Chrysafis Vogiatzis and Jose L. Walteros},
  series = {Lecture Notes in Computer Science},
  year = 2014,
  booktitle = {Learning and Intelligent Optimization, 8th International Conference, LION 8},
  author = { Frank Hutter  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Chris Fawcett  and  Marius Thomas Lindauer  and  Holger H. Hoos  and  Kevin Leyton-Brown  and  Thomas St{\"u}tzle },
  title = {{AClib}: a Benchmark Library for Algorithm
                  Configuration},
  pages = {36--40},
  doi = {10.1007/978-3-319-09584-4_4}
}
@incollection{MasLopDub2014hm,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 8457,
  isbn = {978-3-319-07643-0},
  editor = { Mar{\'\i}a J. Blesa  and  Christian Blum  and  Stefan Vo{\ss} },
  year = 2014,
  booktitle = {Hybrid Metaheuristics},
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Marie-El{\'e}onore Marmion  and  Thomas St{\"u}tzle },
  title = {Algorithm Comparison by Automatically Configurable
                  Stochastic Local Search Frameworks: A Case Study
                  Using Flow-Shop Scheduling Problems},
  pages = {30--44},
  doi = {10.1007/978-3-319-07644-7_3}
}
@incollection{MarMasLop2013hm,
  publisher = {Springer},
  volume = 7919,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'\i}a J. Blesa  and  Christian Blum  and Paola Festa and  Andrea Roli  and  M. Sampels },
  isbn = {978-3-642-38515-5},
  year = 2013,
  booktitle = {Hybrid Metaheuristics},
  author = { Marie-El{\'e}onore Marmion  and  Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Design of Hybrid Stochastic Local Search
                  Algorithms},
  pages = {144--158},
  doi = {10.1007/978-3-642-38516-2_12},
  pdf = {MarMasLopStu2013hm.pdf}
}
@incollection{BezLopStu2013evocop,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  volume = 7832,
  booktitle = {Proceedings of EvoCOP 2013 -- 13th European Conference on Evolutionary Computation in Combinatorial Optimization},
  editor = { M. Middendorf  and  Christian Blum },
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Local Search for the Bi-objective
                  Bidimensional Knapsack Problem},
  pages = {85--96},
  doi = {10.1007/978-3-642-37198-1_8}
}
@incollection{DubLopStu2013hm,
  aurl = {http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-30670-9},
  year = 2013,
  volume = 434,
  series = {Studies in Computational Intelligence},
  editor = { E-G. Talbi },
  publisher = {Springer Verlag},
  booktitle = {Hybrid Metaheuristics},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Combining Two Search Paradigms for Multi-objective
                  Optimization: {T}wo-{P}hase and {P}areto Local Search},
  pages = {97--117},
  doi = {10.1007/978-3-642-30671-6_3},
  alias = {DubLopStu2012hm},
  pdf = {DubLopStu2013hm.pdf}
}
@incollection{MasLopDubStu2013lion,
  publisher = {Springer},
  volume = 7997,
  editor = {P. Pardalos and G. Nicosia},
  series = {Lecture Notes in Computer Science},
  year = 2013,
  booktitle = {Learning and Intelligent Optimization, 7th
                  International Conference, LION 7},
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle },
  title = {From Grammars to Parameters: Automatic Iterated
                  Greedy Design for the Permutation Flow-shop Problem
                  with Weighted Tardiness},
  pages = {321--334},
  pdf = {MasLopDubStu2013lion.pdf},
  doi = {10.1007/978-3-642-44973-4_36}
}
@incollection{MasLopStu2013hm,
  publisher = {Springer},
  volume = 7919,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'\i}a J. Blesa  and  Christian Blum  and Paola Festa and  Andrea Roli  and  M. Sampels },
  isbn = {978-3-642-38515-5},
  year = 2013,
  booktitle = {Hybrid Metaheuristics},
  author = { Florence Massen  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Yves Deville },
  title = {Experimental Analysis of Pheromone-Based Heuristic
                  Column Generation Using irace},
  pages = {92-106},
  doi = {10.1007/978-3-642-38516-2_8},
  pdf = {MasLopStu2013hm.pdf}
}
@incollection{RadLopStu2013emo,
  isbn = {978-3-642-37139-4},
  volume = 7811,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2013},
  editor = {Robin C. Purshouse and Peter J. Fleming and Carlos
                  M. Fonseca and Salvatore Greco and Jane Shaw},
  author = { Andreea Radulescu  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Improving the Anytime Behaviour of
                  Multiobjective Evolutionary Algorithms},
  year = 2013,
  pages = {825--840},
  doi = {10.1007/978-3-642-37140-0_61}
}
@incollection{StuLopPel2011autsea,
  year = 2012,
  address = {Berlin, Germany},
  publisher = {Springer},
  booktitle = {Autonomous Search},
  editor = {Y. Hamadi and E. Monfroy and F. Saubion},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Paola Pellegrini  and  Michael Maur  and  Marco A. {Montes de Oca}  and  Mauro Birattari  and  Marco Dorigo },
  title = {Parameter Adaptation in Ant Colony Optimization},
  doi = {10.1007/978-3-642-21434-9_8},
  pages = {191--215}
}
@incollection{LopLiaStu2012ppsn,
  volume = 7491,
  year = 2012,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature, PPSN XII},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and Liao, Tianjun  and  Thomas St{\"u}tzle },
  title = {On the anytime behavior of {IPOP-CMA-ES}},
  pages = {357--366},
  doi = {10.1007/978-3-642-32937-1_36}
}
@incollection{BezLopStu2012:ants,
  volume = 7461,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  editor = { Marco Dorigo  and others },
  year = 2012,
  booktitle = {Swarm Intelligence, 8th International Conference, ANTS 2012},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Generation of Multi-objective {ACO}
                  Algorithms for the Biobjective Knapsack},
  pages = {37--48},
  doi = {10.1007/978-3-642-32650-9_4},
  pdf = {BezLopStu2012ants.pdf},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-008/}
}
@incollection{DubLopStu2012evocop,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 7245,
  year = 2012,
  editor = { Jin-Kao Hao  and  M. Middendorf },
  booktitle = {Proceedings of EvoCOP 2012 -- 12th European Conference on Evolutionary Computation in Combinatorial Optimization},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {{P}areto Local Search Algorithms for Anytime
                  Bi-objective Optimization},
  pages = {206--217},
  doi = {10.1007/978-3-642-29124-1_18},
  alias = {DubLopStu12:evocop}
}
@incollection{BroLopNau2012ppsn,
  volume = 7491,
  year = 2012,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  editor = { Carlos A. {Coello Coello}  and others},
  booktitle = {Parallel Problem Solving from Nature, PPSN XII},
  author = { Dimo Brockhoff  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Boris Naujoks  and  G{\"u}nther Rudolph },
  title = {Runtime Analysis of Simple Interactive Evolutionary
                  Biobjective Optimization Algorithms},
  pages = {123--132},
  doi = {10.1007/978-3-642-32937-1_13},
  abstract = {Development and deployment of interactive evolutionary
                  multiobjective optimization algorithms (EMOAs) have recently
                  gained broad interest. In this study, first steps towards a
                  theory of interactive EMOAs are made by deriving bounds on
                  the expected number of function evaluations and queries to a
                  decision maker. We analyze randomized local search and the
                  (1+1)-EA on the biobjective problems LOTZ and COCZ under the
                  scenario that the decision maker interacts with these
                  algorithms by providing a subjective preference whenever
                  solutions are incomparable. It is assumed that this decision
                  is based on the decision maker's internal utility
                  function. We show that the performance of the interactive
                  EMOAs may dramatically worsen if the utility function is
                  non-linear instead of linear.}
}
@incollection{AugBroLop2012dagstuhl,
  doi = {10.4230/DagRep.2.1.50},
  series = {Dagstuhl Reports},
  volume = {2(1)},
  year = 2012,
  publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik,
                  Germany},
  booktitle = {Learning in Multiobjective Optimization (Dagstuhl Seminar
                  12041)},
  editor = { Salvatore Greco  and  Joshua D. Knowles  and  Kaisa Miettinen  and  Eckart Zitzler },
  author = { Anne Auger  and  Dimo Brockhoff  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Kaisa Miettinen  and  Boris Naujoks  and  G{\"u}nther Rudolph },
  title = {Which questions should be asked to find the most appropriate
                  method for decision making and problem solving? ({W}orking
                  {G}roup ``{A}lgorithm {D}esign {M}ethods'')},
  pages = {92--93}
}
@incollection{StuLopDor2011eorms,
  year = 2011,
  publisher = {John Wiley \& Sons},
  editor = {J. J. Cochran},
  booktitle = {Wiley Encyclopedia of Operations Research and
                  Management Science},
  author = { Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Marco Dorigo },
  title = {A Concise Overview of Applications of Ant Colony
                  Optimization},
  pages = {896--911},
  volume = 2,
  doi = {10.1002/9780470400531.eorms0001}
}
@incollection{EppLopStuDeS2011:cec,
  year = 2011,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of the 2011 Congress on Evolutionary
                  Computation (CEC 2011)},
  author = { Stefan Eppe  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle  and  Yves {De Smet} },
  title = {An Experimental Study of Preference Model Integration into
                  Multi-Objective Optimization Heuristics},
  pages = {2751--2758},
  doi = {10.1109/CEC.2011.5949963}
}
@incollection{LopKnoLau2011emo,
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2011},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Joshua D. Knowles  and  Marco Laumanns },
  title = {On Sequential Online Archiving of Objective Vectors},
  pages = {46--60},
  doi = {10.1007/978-3-642-19893-9_4},
  abstract = {In this paper, we examine the problem of maintaining
                  an approximation of the set of nondominated points
                  visited during a multiobjective optimization, a
                  problem commonly known as archiving. Most of the
                  currently available archiving algorithms are
                  reviewed, and what is known about their convergence
                  and approximation properties is summarized. The main
                  scenario considered is the restricted case where the
                  archive must be updated online as points are
                  generated one by one, and at most a fixed number of
                  points are to be stored in the archive at any one
                  time. In this scenario, the better-monotonicity of
                  an archiving algorithm is proposed as a weaker, but
                  more practical, property than negative efficiency
                  preservation. This paper shows that
                  hypervolume-based archivers and a recently proposed
                  multi-level grid archiver have this property. On the
                  other hand, the archiving methods used by SPEA2 and
                  NSGA-II do not, and they may better-deteriorate with
                  time. The better-monotonicity property has meaning
                  on any input sequence of points. We also classify
                  archivers according to limit properties,
                  i.e. convergence and approximation properties of the
                  archiver in the limit of infinite (input) samples
                  from a finite space with strictly positive
                  generation probabilities for all points. This paper
                  establishes a number of research questions, and
                  provides the initial framework and analysis for
                  answering them.},
  annote = {Revised version available at \url{http://iridia.ulb.ac.be/IridiaTrSeries/IridiaTr2011-001.pdf}}
}
@incollection{DubLopStu2011gecco,
  isbn = {978-1-4503-0557-0},
  address = {New York, NY},
  publisher = {ACM Press},
  year = 2011,
  editor = {Natalio Krasnogor and Pier Luca Lanzi},
  booktitle = {Proceedings of the Genetic and Evolutionary
                  Computation Conference, GECCO 2011},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic configuration of state-of-the-art multi-objective
                  optimizers using the {TP+PLS} framework},
  pages = {2019--2026},
  doi = {10.1145/2001576.2001847}
}
@incollection{BluLop2011ieh,
  author = { Christian Blum  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  booktitle = {The Industrial Electronics Handbook: Intelligent
                  Systems},
  title = {Ant Colony Optimization},
  publisher = {CRC Press},
  year = {2011},
  edition = {Second},
  isbn = {9781439802830},
  url = {http://www.crcpress.com/product/isbn/9781439802830},
  annnote = {http://www.eng.auburn.edu/~wilambm/ieh/}
}
@incollection{FonGueLopPaq2011emo,
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2011},
  author = { Carlos M. Fonseca  and  Andreia P. Guerreiro  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete },
  title = {On the Computation of the Empirical Attainment Function},
  doi = {10.1007/978-3-642-19893-9_8},
  pages = {106--120}
}
@incollection{MauLopStu2010:cec,
  year = 2010,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  booktitle = {Proceedings of the 2010 Congress on Evolutionary
                  Computation (CEC 2010)},
  editor = {Ishibuchi, H. and others},
  author = { Michael Maur  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Pre-scheduled and adaptive parameter variation in
                  {\MaxMinAntSystem}},
  pages = {3823--3830},
  doi = {10.1109/CEC.2010.5586332}
}
@incollection{LopStu09ea,
  publisher = {Springer},
  editor = {Pierre Collet and Nicolas Monmarch{\'e} and Pierrick
                  Legrand and Marc Schoenauer and Evelyne Lutton},
  shorteditor = {Pierre Collet and others},
  volume = 5975,
  series = {Lecture Notes in Computer Science},
  year = 2010,
  booktitle = {Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An Analysis of Algorithmic Components for
                  Multiobjective Ant Colony Optimization: {A} Case
                  Study on the Biobjective {TSP}},
  pages = {134--145},
  doi = {10.1007/978-3-642-14156-0_12}
}
@incollection{LopStu2010:ants,
  volume = 6234,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer},
  fulleditor = { Marco Dorigo  and  Mauro Birattari  and  Gianni A. {Di Caro}  and Doursat, R. and Engelbrecht, A. P. and Floreano,
                  D. and Gambardella, L. M. and Gro\ss, R. and Sahin,
                  E. and  Thomas St{\"u}tzle  and Sayama, H.},
  editor = { Marco Dorigo  and others },
  year = 2010,
  booktitle = {Swarm Intelligence, 7th International Conference, ANTS 2010},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Configuration of Multi-Objective {ACO}
                  Algorithms},
  pages = {95--106},
  doi = {10.1007/978-3-642-15461-4_9},
  abstract = {In the last few years a significant number of ant
                  colony optimization (ACO) algorithms have been
                  proposed for tackling multi-objective optimization
                  problems. In this paper, we propose a software
                  framework that allows to instantiate the most
                  prominent multi-objective ACO (MOACO)
                  algorithms. More importantly, the flexibility of
                  this MOACO framework allows the application of
                  automatic algorithm configuration techniques. The
                  experimental results presented in this paper show
                  that such an automatic configuration of MOACO
                  algorithms is highly desirable, given that our
                  automatically configured algorithms clearly
                  outperform the best performing MOACO algorithms that
                  have been proposed in the literature. As far as we
                  are aware, this paper is also the first to apply
                  automatic algorithm configuration techniques to
                  multi-objective stochastic local search algorithms.}
}
@incollection{LopStu2010:gecco,
  address = {New York, NY},
  publisher = {ACM Press},
  year = 2010,
  booktitle = {Proceedings of the Genetic and Evolutionary
                  Computation Conference, GECCO 2010},
  editor = {Martin Pelikan and  J{\"u}rgen Branke },
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The impact of design choices of multi-objective ant
                  colony optimization algorithms on performance: An
                  experimental study on the biobjective {TSP}},
  doi = {10.1145/1830483.1830494},
  pages = {71--78},
  abstract = {Over the last few years, there have been a number of
                  proposals of ant colony optimization (ACO)
                  algorithms for tackling multiobjective combinatorial
                  optimization problems. These proposals adapt ACO
                  concepts in various ways, for example, some use
                  multiple pheromone matrices and multiple heuristic
                  matrices and others use multiple ant colonies.\\ In
                  this article, we carefully examine several of the
                  most prominent of these proposals. In particular, we
                  identify commonalities among the approaches by
                  recasting the original formulation of the algorithms
                  in different terms. For example, several proposals
                  described in terms of multiple colonies can be cast
                  equivalently using a single ant colony, where ants
                  use different weights for aggregating the pheromone
                  and/or the heuristic information. We study
                  algorithmic choices for the various proposals and we
                  identify previously undetected trade-offs in their
                  performance.}
}
@incollection{LopPaqStu09emaa,
  editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
  year = 2010,
  address = {Berlin, Germany},
  publisher = {Springer},
  booktitle = {Experimental Methods for the Analysis of
                  Optimization Algorithms},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Exploratory Analysis of Stochastic Local Search
                  Algorithms in Biobjective Optimization},
  pages = {209--222},
  doi = {10.1007/978-3-642-02538-9_9},
  abstract = {This chapter introduces two Perl programs that
                  implement graphical tools for exploring the
                  performance of stochastic local search algorithms
                  for biobjective optimization problems. These tools
                  are based on the concept of the empirical attainment
                  function (EAF), which describes the probabilistic
                  distribution of the outcomes obtained by a
                  stochastic algorithm in the objective space. In
                  particular, we consider the visualization of
                  attainment surfaces and differences between the
                  first-order EAFs of the outcomes of two
                  algorithms. This visualization allows us to identify
                  certain algorithmic behaviors in a graphical way.
                  We explain the use of these visualization tools and
                  illustrate them with examples arising from
                  practice.}
}
@incollection{DubLopStu10:lion-bfsp,
  publisher = {Springer},
  editor = { Christian Blum  and  Roberto Battiti },
  series = {Lecture Notes in Computer Science},
  volume = 6073,
  year = 2010,
  booktitle = {Learning and Intelligent Optimization, 4th
                  International Conference, LION 4},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Adaptive ``Anytime'' Two-Phase Local Search},
  pages = {52--67},
  doi = {10.1007/978-3-642-13800-3_5}
}
@incollection{LopBlu09:evocop,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 5482,
  year = 2009,
  editor = { Carlos Cotta  and P. Cowling},
  booktitle = {Proceedings of EvoCOP 2009 -- 9th European Conference on Evolutionary Computation in Combinatorial Optimization},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum  and  Dhananjay Thiruvady  and  Andreas T. Ernst  and  Bernd Meyer },
  title = {Beam-{ACO} based on stochastic sampling for makespan
                  optimization concerning the {TSP} with time windows},
  pages = {97--108},
  pdf = {LopBlu09-Beam-ACO-TSPTW-evocop.pdf},
  doi = {10.1007/978-3-642-01009-5_9},
  alias = {Lop++09}
}
@incollection{LopBlu09:lion,
  publisher = {Springer},
  year = 2009,
  editor = { Thomas St{\"u}tzle },
  volume = 5851,
  series = {Lecture Notes in Computer Science},
  booktitle = {Learning and Intelligent Optimization, Third International Conference, LION 3},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum },
  title = {Beam-{ACO} Based on Stochastic Sampling: {A} Case
                  Study on the {TSP} with Time Windows},
  pages = {59--73},
  doi = {10.1007/978-3-642-11169-3_5}
}
@incollection{DubLopStu09:hm-bfsp,
  publisher = {Springer},
  volume = 5818,
  series = {Lecture Notes in Computer Science},
  editor = { Mar{\'\i}a J. Blesa  and  Christian Blum  and Luca {Di Gaspero} and  Andrea Roli  and  M. Sampels  and Andrea Schaerf},
  year = 2009,
  booktitle = {Hybrid Metaheuristics},
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Effective Hybrid Stochastic Local Search Algorithms
                  for Biobjective Permutation Flowshop Scheduling},
  pages = {100--114},
  pdf = {DubLopStu09hm-bfsp.pdf},
  doi = {10.1007/978-3-642-04918-7_8},
  alias = {DuboisHM09}
}
@incollection{LopPraPae:gecco07,
  address = {New York, NY},
  publisher = {ACM Press},
  year = 2007,
  editor = {Dirk Thierens and others},
  booktitle = {Proceedings of the Genetic and Evolutionary Computation
                  Conference, GECCO 2007},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Solving Optimal Pump Control Problem using
                  {\MaxMinAntSystem}},
  volume = 1,
  pages = 176,
  doi = {10.1145/1276958.1276990},
  pdf = {pap212s1-lopezibanez.pdf}
}
@incollection{PaqStuLop07metaheuristics,
  author = { Lu{\'i}s Paquete  and  Thomas St{\"u}tzle  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Using experimental design to analyze stochastic
                  local search algorithms for multiobjective problems},
  booktitle = {Metaheuristics: Progress in Complex Systems
                  Optimization},
  pages = {325--344},
  year = 2007,
  doi = {10.1007/978-0-387-71921-4_17},
  volume = 39,
  series = {Operations Research / Computer Science Interfaces},
  publisher = {Springer, New York, NY},
  annote = {Post-Conference Proceedings of the 6th
                  Metaheuristics International Conference (MIC 2005)},
  editor = {Karl F. Doerner and Michel Gendreau and Peter
                  Greistorfer and  Gutjahr, Walter J.  and  Richard F. Hartl and  Marc Reimann },
  abstract = {Stochastic Local Search (SLS) algorithms can be seen
                  as being composed of several algorithmic components,
                  each playing some specific role with respect to
                  overall performance. This article explores the
                  application of experimental design techniques to
                  analyze the effect of components of SLS algorithms
                  for Multiobjective Combinatorial Optimization
                  problems, in particular for the Biobjective
                  Quadratic Assignment Problem. The analysis shows
                  that there exists a strong dependence between the
                  choices for these components and various instance
                  features, such as the structure of the input data
                  and the correlation between the objectives.}
}
@incollection{FonPaqLop06:hypervolume,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = jul,
  year = 2006,
  booktitle = {Proceedings of the 2006 Congress on Evolutionary
                  Computation (CEC 2006)},
  author = { Carlos M. Fonseca  and  Lu{\'i}s Paquete  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {An improved dimension\hspace{0pt}-\hspace{0pt}sweep
                  algorithm for the hypervolume indicator},
  pages = {1157--1163},
  doi = {10.1109/CEC.2006.1688440},
  pdf = {FonPaqLop06-hypervolume.pdf},
  abstract = {This paper presents a recursive, dimension-sweep
                  algorithm for computing the hypervolume indicator of
                  the quality of a set of $n$ non-dominated points in
                  $d>2$ dimensions. It improves upon the existing HSO
                  (Hypervolume by Slicing Objectives) algorithm by
                  pruning the recursion tree to avoid repeated
                  dominance checks and the recalculation of partial
                  hypervolumes. Additionally, it incorporates a recent
                  result for the three-dimensional special case.  The
                  proposed algorithm achieves $O(n^{d-2} \log n)$ time
                  and linear space complexity in the worst-case, but
                  experimental results show that the pruning
                  techniques used may reduce the time complexity
                  exponent even further.}
}
@incollection{LopPraPaech05:cec,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = sep,
  year = 2005,
  booktitle = {Proceedings of the 2005 Congress on Evolutionary
                  Computation (CEC 2005)},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Multi-objective Optimisation of the Pump Scheduling
                  Problem using {SPEA2}},
  pages = {435--442},
  volume = 1,
  doi = {10.1109/CEC.2005.1554716}
}
@incollection{LopPaqStu04:ants,
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  volume = 3172,
  editor = { Marco Dorigo  and others },
  fulleditor = { Marco Dorigo  and  L. M. Gambardella  and  F. Mondada  and  Thomas St{\"u}tzle  and  Mauro Birattari  and  Christian Blum },
  year = 2004,
  booktitle = {Ant Colony Optimization and Swarm Intelligence, 4th
                  International Workshop, ANTS 2004},
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {On the Design of {ACO} for the Biobjective Quadratic
                  Assignment Problem},
  pages = {214--225},
  doi = {10.1007/978-3-540-28646-2_19}
}
@article{PerLopStu2015si,
  author = {Leslie {P{\'e}rez C{\'a}ceres}  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Ant colony optimization on a limited budget of evaluations},
  journal = {Swarm Intelligence},
  year = 2015,
  doi = {10.1007/s11721-015-0106-x},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2015-004},
  pdf = {PerLopStu2015si.pdf},
  volume = 9,
  number = {2-3},
  pages = {103--124}
}
@article{TriLop2015plos,
  author = { Vito Trianni  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Advantages of Task-Specific Multi-Objective Optimisation in
                  Evolutionary Robotics},
  journal = {PLoS One},
  year = 2015,
  volume = 10,
  number = 8,
  pages = {e0136406},
  doi = {10.1371/journal.pone.0136406},
  abstract = {The application of multi-objective optimisation to
                  evolutionary robotics is receiving increasing attention. A
                  survey of the literature reveals the different possibilities
                  it offers to improve the automatic design of efficient and
                  adaptive robotic systems, and points to the successful
                  demonstrations available for both task-specific and
                  task-agnostic approaches (i.e., with or without reference to
                  the specific design problem to be tackled). However, the
                  advantages of multi-objective approaches over
                  single-objective ones have not been clearly spelled out and
                  experimentally demonstrated. This paper fills this gap for
                  task-specific approaches: starting from well-known results in
                  multi-objective optimisation, we discuss how to tackle
                  commonly recognised problems in evolutionary robotics. In
                  particular, we show that multi-objective optimisation (i)
                  allows evolving a more varied set of behaviours by exploring
                  multiple trade-offs of the objectives to optimise, (ii)
                  supports the evolution of the desired behaviour through the
                  introduction of objectives as proxies, (iii) avoids the
                  premature convergence to local optima possibly introduced by
                  multi-component fitness functions, and (iv) solves the
                  bootstrap problem exploiting ancillary objectives to guide
                  evolution in the early phases. We present an experimental
                  demonstration of these benefits in three different case
                  studies: maze navigation in a single robot domain, flocking
                  in a swarm robotics context, and a strictly collaborative
                  task in collective robotics.}
}
@article{BezLopStu2015tec,
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatic Component-Wise Design of Multi-Objective
                  Evolutionary Algorithms},
  journal = {IEEE Transactions on Evolutionary Computation},
  doi = {10.1109/TEVC.2015.2474158},
  year = 2015,
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2014-010/},
  pdf = {BezLopStu2015tec.pdf}
}
@article{DubLopStu2015ejor,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Anytime {P}areto Local Search},
  journal = {European Journal of Operational Research},
  year = 2015,
  volume = 243,
  number = 2,
  pages = {369--385},
  doi = {10.1016/j.ejor.2014.10.062},
  pdf = {DubLopStu2015ejor.pdf},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2013-003/},
  alias = {DubLopStu2013cor}
}
@article{LopStu2013ejor,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Automatically Improving the Anytime Behaviour of
                  Optimisation Algorithms},
  journal = {European Journal of Operational Research},
  year = 2014,
  volume = 235,
  number = 3,
  pages = {569--582},
  doi = {10.1016/j.ejor.2013.10.043},
  pdf = {LopStu2014ejor.pdf},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-011/},
  abstract = {Optimisation algorithms with good anytime behaviour try to return
  as high-quality solutions as possible independently of the
  computation time allowed. Designing algorithms with good anytime
  behaviour is a difficult task, because performance is often
  evaluated subjectively, by plotting the trade-off curve between
  computation time and solution quality. Yet, the trade-off curve may
  be modelled also as a set of mutually nondominated, bi-objective
  points. Using this model, we propose to combine an automatic
  configuration tool and the hypervolume measure, which assigns a
  single quality measure to a nondominated set. This allows us to
  improve the anytime behaviour of optimisation algorithms by means of
  automatically finding algorithmic configurations that produce the
  best nondominated sets. Moreover, the recently proposed weighted
  hypervolume measure is used here to incorporate the decision-maker's
  preferences into the automatic tuning procedure. We report on the
  improvements reached when applying the proposed method to two
  relevant scenarios: (i) the design of parameter variation strategies
  for MAX-MIN Ant System and (ii) the tuning of the anytime behaviour
  of SCIP, an open-source mixed integer programming solver with more
  than 200 parameters.}
}
@article{MasLopDubStu2014cor,
  author = { Franco Mascia  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  J{\'e}r{\'e}mie Dubois-Lacoste  and  Thomas St{\"u}tzle },
  title = {Grammar-based generation of stochastic local search
                  heuristics through automatic algorithm configuration tools},
  journal = {Computers \& Operations Research},
  year = 2014,
  doi = {10.1016/j.cor.2014.05.020},
  pdf = {MasLopDubStu2014cor.pdf},
  volume = 51,
  pages = {190--199}
}
@article{LopBlu2013asoc,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum  and  Jeffrey W. Ohlmann  and  Barrett W. Thomas },
  title = {The Travelling Salesman Problem with Time Windows:
                  Adapting Algorithms from Travel-time to Makespan
                  Optimization},
  journal = {Applied Soft Computing},
  year = 2013,
  volume = 13,
  number = 9,
  pages = {3806--3815},
  doi = {10.1016/j.asoc.2013.05.009},
  pdf = {http://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2013-011.pdf}
}
@article{LopStu2012swarm,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {An experimental analysis of design choices of multi-objective ant colony optimization algorithms},
  journal = {Swarm Intelligence},
  year = 2012,
  number = 3,
  volume = 6,
  pages = {207--232},
  doi = {10.1007/s11721-012-0070-7},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2012-006/}
}
@article{LopStu2012tec,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {The Automatic Design of Multi-Objective Ant Colony
                  Optimization Algorithms},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2012,
  volume = 16,
  number = 6,
  pages = {861--875},
  doi = {10.1109/TEVC.2011.2182651},
  abstract = {
Multi-objective optimization problems are problems with several,
typically conflicting criteria for evaluating solutions. Without
any a priori preference information, the Pareto optimality
principle establishes a partial order among solutions, and the
output of the algorithm becomes a set of nondominated solutions
rather than a single one. Various ant colony optimization (ACO)
algorithms have been proposed in recent years for solving such
problems. These multi-objective ACO (MOACO) algorithms exhibit
different design choices for dealing with the particularities of
the multi-objective context. This paper proposes a formulation of
algorithmic components that suffices to describe most MOACO
algorithms proposed so far. This formulation also shows that
existing MOACO algorithms often share equivalent design choices
but they are described in different terms. Moreover, this
formulation is synthesized into a flexible algorithmic framework,
from which not only existing MOACO algorithms may be
instantiated, but also combinations of components that were never
studied in the literature. In this sense, this paper goes beyond
proposing a new MOACO algorithm, but it rather introduces a
family of MOACO algorithms. The flexibility of the proposed MOACO
framework facilitates the application of automatic algorithm
configuration techniques. The experimental results presented in
this paper show that the automatically configured MOACO framework
outperforms the MOACO algorithms that inspired the framework
itself. This paper is also among the first to apply automatic
algorithm configuration techniques to multi-objective algorithms.}
}
@article{LopPraPae2011ec,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Representations and Evolutionary Operators for the
                  Scheduling of Pump Operations in Water Distribution
                  Networks},
  journal = {Evolutionary Computation},
  year = 2011,
  doi = {10.1162/EVCO_a_00035},
  volume = 19,
  number = 3,
  pages = {429--467},
  abstract = {Reducing the energy consumption of water
                  distribution networks has never had more
                  significance. The greatest energy savings can be
                  obtained by carefully scheduling the operations of
                  pumps. Schedules can be defined either implicitly,
                  in terms of other elements of the network such as
                  tank levels, or explicitly by specifying the time
                  during which each pump is on/off.  The traditional
                  representation of explicit schedules is a string of
                  binary values with each bit representing pump on/off
                  status during a particular time interval.  In this
                  paper, we formally define and analyze two new
                  explicit representations based on time-controlled
                  triggers, where the maximum number of pump switches
                  is established beforehand and the schedule may
                  contain less switches than the maximum. In these
                  representations, a pump schedule is divided into a
                  series of integers with each integer representing
                  the number of hours for which a pump is
                  active/inactive.  This reduces the number of
                  potential schedules compared to the binary
                  representation, and allows the algorithm to operate
                  on the feasible region of the search space.  We
                  propose evolutionary operators for these two new
                  representations. The new representations and their
                  corresponding operations are compared with the two
                  most-used representations in pump scheduling,
                  namely, binary representation and level-controlled
                  triggers. A detailed statistical analysis of the
                  results indicates which parameters have the greatest
                  effect on the performance of evolutionary
                  algorithms. The empirical results show that an
                  evolutionary algorithm using the proposed
                  representations improves over the results obtained
                  by a recent state-of-the-art Hybrid Genetic
                  Algorithm for pump scheduling using level-controlled
                  triggers.}
}
@article{DubLopStu2011amai,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {Improving the Anytime Behavior of Two-Phase Local
                  Search},
  journal = {Annals of Mathematics and Artificial Intelligence},
  year = 2011,
  volume = 61,
  number = 2,
  pages = {125--154},
  doi = {10.1007/s10472-011-9235-0},
  alias = {DubLopStu2010amai},
  pdf = {DubLopStu2011amai.pdf}
}
@article{DubLopStu2011cor,
  author = { J{\'e}r{\'e}mie Dubois-Lacoste  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Thomas St{\"u}tzle },
  title = {A Hybrid {TP$+$PLS} Algorithm for Bi-objective
                  Flow-Shop Scheduling Problems},
  journal = {Computers \& Operations Research},
  year = 2011,
  volume = 38,
  number = 8,
  pages = {1219--1236},
  doi = {10.1016/j.cor.2010.10.008},
  supplement = {http://iridia.ulb.ac.be/supp/IridiaSupp2010-001/},
  pdf = {DubLopStu2011cor.pdf}
}
@article{LopBlu2010cor,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Christian Blum },
  title = {Beam-{ACO} for the travelling salesman problem with
                  time windows},
  journal = {Computers \& Operations Research},
  year = 2010,
  doi = {10.1016/j.cor.2009.11.015},
  volume = 37,
  number = 9,
  pages = {1570--1583},
  keywords = {Ant colony optimization},
  keywords = {Travelling salesman problem with time windows},
  keywords = {Hybridization},
  alias = {LopBlu09tsptw},
  abstract = {The travelling salesman problem with time windows is
                  a difficult optimization problem that arises, for
                  example, in logistics. This paper deals with the
                  minimization of the travel-cost. For solving this
                  problem, this paper proposes a Beam-ACO algorithm,
                  which is a hybrid method combining ant colony
                  optimization with beam search.  In general, Beam-ACO
                  algorithms heavily rely on accurate and
                  computationally inexpensive bounding information for
                  differentiating between partial solutions. This work
                  uses stochastic sampling as a useful alternative. An
                  extensive experimental evaluation on seven benchmark
                  sets from the literature shows that the proposed
                  Beam-ACO algorithm is currently a state-of-the-art
                  technique for the travelling salesman problem with
                  time windows when travel-cost optimization is
                  concerned.}
}
@article{BeuFonLopPaqVah09:tec,
  author = { Nicola Beume  and  Carlos M. Fonseca  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Jan Vahrenhold },
  title = {On the complexity of computing the hypervolume
                  indicator},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2009,
  volume = 13,
  number = 5,
  pages = {1075--1082},
  doi = {10.1109/TEVC.2009.2015575},
  abstract = {The goal of multi-objective optimization is to find
                  a set of best compromise solutions for typically
                  conflicting objectives. Due to the complex nature of
                  most real-life problems, only an approximation to
                  such an optimal set can be obtained within
                  reasonable (computing) time. To compare such
                  approximations, and thereby the performance of
                  multi-objective optimizers providing them, unary
                  quality measures are usually applied. Among these,
                  the \emph{hypervolume indicator} (or
                  \emph{S-metric}) is of particular relevance due to
                  its favorable properties. Moreover, this indicator
                  has been successfully integrated into stochastic
                  optimizers, such as evolutionary algorithms, where
                  it serves as a guidance criterion for finding good
                  approximations to the Pareto front. Recent results
                  show that computing the hypervolume indicator can be
                  seen as solving a specialized version of Klee's
                  Measure Problem.  In general, Klee's Measure Problem
                  can be solved with $\mathcal{O}(n \log n +
                  n^{d/2}\log n)$ comparisons for an input instance of
                  size $n$ in $d$ dimensions; as of this writing, it
                  is unknown whether a lower bound higher than
                  $\Omega(n \log n)$ can be proven. In this article,
                  we derive a lower bound of $\Omega(n\log n)$ for the
                  complexity of computing the hypervolume indicator in
                  any number of dimensions $d>1$ by reducing the
                  so-called \textsc{UniformGap} problem to it.  For
                  the three dimensional case, we also present a
                  matching upper bound of $\mathcal{O}(n\log n)$
                  comparisons that is obtained by extending an
                  algorithm for finding the maxima of a point set.}
}
@article{BluBleLop09-BeamSearch-LCS,
  author = { Christian Blum  and  Mar{\'\i}a J. Blesa  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez },
  title = {Beam search for the longest common subsequence
                  problem},
  number = 12,
  journal = {Computers \& Operations Research},
  year = 2009,
  pages = {3178--3186},
  volume = 36,
  doi = {10.1016/j.cor.2009.02.005},
  pdf = {BluBleLop09-BeamSearch-LCS.pdf},
  abstract = { The longest common subsequence problem is a
                  classical string problem that concerns finding the
                  common part of a set of strings. It has several
                  important applications, for example, pattern
                  recognition or computational biology. Most research
                  efforts up to now have focused on solving this
                  problem optimally. In comparison, only few works
                  exist dealing with heuristic approaches. In this
                  work we present a deterministic beam search
                  algorithm. The results show that our algorithm
                  outperforms the current state-of-the-art approaches
                  not only in solution quality but often also in
                  computation time.}
}
@article{LopPraPae08aco,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  T. Devi Prasad  and  Ben Paechter },
  title = {Ant Colony Optimisation for the Optimal Control of
                  Pumps in Water Distribution Networks},
  journal = {Journal of Water Resources Planning and Management, {ASCE}},
  year = 2008,
  volume = 134,
  number = 4,
  pages = {337--346},
  publisher = {{ASCE}},
  pdf = {LopezPrasadPaechter08-jwrpm.pdf},
  aurl = {http://link.aip.org/link/?QWR/134/337/1},
  doi = {10.1061/(ASCE)0733-9496(2008)134:4(337)},
  abstract = {Reducing energy consumption of water distribution
  networks has never had more significance than today. The greatest
  energy savings can be obtained by careful scheduling of operation of
  pumps. Schedules can be defined either implicitly, in terms of other
  elements of the network such as tank levels, or explicitly by
  specifying the time during which each pump is on/off. The
  traditional representation of explicit schedules is a string of
  binary values with each bit representing pump on/off status during a
  particular time interval. In this paper a new explicit
  representation is presented. It is based on time controlled
  triggers, where the maximum number of pump switches is specified
  beforehand. In this representation a pump schedule is divided into a
  series of integers with each integer representing the number of
  hours for which a pump is active/inactive. This reduces the number
  of potential schedules (search space) compared to the binary
  representation. Ant colony optimization (ACO) is a stochastic
  meta-heuristic for combinatorial optimization problems that is
  inspired by the foraging behavior of some species of ants. In this
  paper, an application of the ACO framework was developed for the
  optimal scheduling of pumps. The proposed representation was adapted
  to an ant colony Optimization framework and solved for the optimal
  pump schedules. Minimization of electrical cost was considered as
  the objective, while satisfying system constraints. Instead of using
  a penalty function approach for constraint violations, constraint
  violations were ordered according to their importance and solutions
  were ranked based on this order. The proposed approach was tested on
  a small test network and on a large real-world network. Results are
  compared with those obtained using a simple genetic algorithm based
  on binary representation and a hybrid genetic algorithm that uses
  level-based triggers.}
}
@article{LopPaqStu05:jmma,
  author = { Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Hybrid Population-based Algorithms for the
                  Bi-objective Quadratic Assignment Problem},
  journal = {Journal of Mathematical Modelling and Algorithms},
  year = 2006,
  volume = 5,
  number = 1,
  pages = {111--137},
  pdf = {LopPaqStu04-techrepAIDA-04-11.pdf},
  doi = {10.1007/s10852-005-9034-x},
  alias = {LopPaqStu06:jmma},
  abstract = {We present variants of an ant colony optimization
                  (MO-ACO) algorithm and of an evolutionary algorithm
                  (SPEA2) for tackling multi-objective combinatorial
                  optimization problems, hybridized with an iterative
                  improvement algorithm and the robust tabu search
                  algorithm. The performance of the resulting hybrid
                  stochastic local search (SLS) algorithms is
                  experimentally investigated for the bi-objective
                  quadratic assignment problem (bQAP) and compared
                  against repeated applications of the underlying
                  local search algorithms for several
                  scalarizations. The experiments consider structured
                  and unstructured bQAP instances with various degrees
                  of correlation between the flow matrices. We do a
                  systematic experimental analysis of the algorithms
                  using outperformance relations and the attainment
                  functions methodology to asses differences in the
                  performance of the algorithms. The experimental
                  results show the usefulness of the hybrid algorithms
                  if the available computation time is not too limited
                  and identify SPEA2 hybridized with very short tabu
                  search runs as the most promising variant.}
}