A Java implementation of CCMGPSO, an algorithm for solving large-scale multi-objective optimization problems.
This repo contains a Java implementation of our algorithm CCMGPSO, an algorithm for solving large-scale multi-objective optimization problems. This algorithm was born out of our research on large-scale multi-objective problems, and it showed competitive performance for a variety of separable and non-separable problems with many decision variables and two to three objectives. The findings of our research were published in the Swarm and Evolutionary Computation journal in 2023.
Many problems that are encountered in real-life applications consist of two or three conflicting objectives and many decision variables. Multi-guide particle swarm optimization (MGPSO) is a novel meta-heuristic for multi-objective optimization based on particle swarm optimization (PSO). MGPSO has been shown to be competitive when compared with other state-of-the-art multi-objective optimization algorithms for low-dimensional (and even many-objective) problems. However, a recent study has shown that MGPSO does not scale well when the number of decision variables is increased. This paper proposes a new scalable MGPSO-based algorithm, termed cooperative coevolutionary multi-guide particle swarm optimization (abbreviated as CCMGPSO), that incorporates ideas from cooperative coevolution (CC). CCMGPSO uses new techniques to spend less computational budget by periodically assigning only one CC-based subswarm to each objective (as opposed to using numerous CC-based subswarms). Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems with reference to the inverted generational distance (IGD) metric.
A preprint of the paper is avaible on this repo. To access the preprint, please refer to CCMGPSO/ccmgpso_manuscript.pdf.
If you are doing research on optimization or other relevant topics and you want to use this repo to include CCMGPSO in your experimental studies, please include a link to this repo in your bibliography or somewhere in the paper (e.g., in a footnote). Furthermore, please cite CCMGPSO as:
@article{madani2023cooperative,
author = {Amirali Madani and
Andries P. Engelbrecht and
Beatrice M. Ombuki{-}Berman},
title = {Cooperative coevolutionary multi-guide particle swarm optimization
algorithm for large-scale multi-objective optimization problems},
journal = {Swarm and Evolutionary Computation},
volume = {78},
pages = {101262},
year = {2023},
url = {https://doi.org/10.1016/j.swevo.2023.101262},
doi = {10.1016/J.SWEVO.2023.101262},
timestamp = {Tue, 06 Jun 2023 10:54:20 +0200},
biburl = {https://dblp.org/rec/journals/swevo/MadaniEO23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}