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Cooperative coevolutionary multi-guide particle swarm optimization algorithm for large-scale multi-objective optimization problems
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DOI：10.1016/j.swevo.2023.101262.png)
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Abstract
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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 (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). A detailed empirical study on well-known benchmark problems comparing the CCMGPSO with various state-of-the-art large-scale multi-objective optimization algorithms is done. Results show that the proposed CCMGPSO is highly competitive for high-dimensional problems with reference to the inverted generational distance (IGD) metric.
Keywords:
Particle swarm optimization
Multi-objective optimization
Multi-objective evolutionary algorithms
Large-scale multi-objective optimization
Cooperative coevolution
Multi-guide particle swarm optimization
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