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. Joshua Knowles and David Corne have made available several papers on the subject of bounded archiving. We consider here 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.
We provide a program that implements most of the currently available archiving algorithms (archivers) in a common framework for simplifying their comparison and analysis.
We have also made available several benchmark sequences of objective vectors for testing these archivers.
The program is implemented in C++ and can be compiled from source by invoking
The program reads a file containing a sequence of objective vectors. Each objective vector appears in a different line and the objectives are columns separated by whitespace. An example of invocation would be
archiver -f sequence.txt -t 1 -N 10
We also provide examples of benchmark sequences for testing the archivers.
The other options available are given by the output of
-t integer : archive type 0 Unbound Archiver 1 Dominating Archiver 2 ePareto Archiver 3 e-approx Archiver 4 SPEA2 Archiver 5 NSGA2 Archiver 6 Adaptive Grid Archiver (AGA) 7 Hypervolume Archiver (AA_S) 8 Multilevel Grid Archiver (MGA) -f character string : file name of sequence data -N positive integer : capacity of the archive -len positive integer : length of the input sequence -s positive long : random seed -o character string: output filename for sequence output, otherwise, print only the final result to stdout. -g positive integer : number of levels of the adaptive grid; #grid regions=2^(l*k) -e positive float : epsilon value for epsilon archivers -v : print version and copyright information
This program is free software (software libre); you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
IMPORTANT NOTE: Please be aware that the fact that this program is released as Free Software does not excuse you from scientific propriety, which obligates you to give appropriate credit! If you write a scientific paper describing research that made substantive use of this program, it is your obligation as a scientist to (a) mention the fashion in which this software was used in the Methods section; (b) mention the algorithm in the References section. The appropriate citation is:
López-Ibáñez, Joshua D. Knowles, and Marco
Laumanns. On Sequential Online Archiving of Objective Vectors. In
R. Takahashi et al., editors, Evolutionary Multi-criterion
Optimization (EMO 2011), volume 6576 of Lecture Notes in Computer
Science, pages 46–60. Springer, Heidelberg, Germany,
Moreover, as a personal note, we would appreciate it if you would email
manuel.lopez-ibanezulb.ac.be with citations of papers referencing this
work so we can mention them to our funding agent and/or tenure