Cooperative Versus Competitive Coevolution for Pareto Multiobjective Optimization

• Published in: Bio-Inspired Computational Intelligence and Applications Vol. 4688; pp. 63 - 72
• Main Authors: Tan, Tse Guan, Lau, Hui Keng, Teo, Jason
• Format: Book Chapter
• Language: English
• Published: Germany Springer Berlin / Heidelberg 2007; Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Generational Distance; Multiobjective Evolutionary Algorithm; Multiobjective Genetic Algorithm; Multiobjective Optimization; Nondominated Solution
• ISBN: 3540747680; 9783540747680
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-74769-7_8

In this paper, we propose the integration between Strength Pareto Evolutionary Algorithm 2 (SPEA2) with two types of coevolution concept, Competitive Coevolution (CE) and Cooperative Coevolution (CC), to solve 3 dimensional multiobjective optimization problems. The resulting algorithms are referred to as Strength Pareto Evolutionary Algorithm 2 with Competitive Coevolution (SPEA2-CE) and Strength Pareto Evolutionary Algorithm 2 with Cooperative Coevolution (SPEA2-CC). The main objective of this paper is to compare competitive against cooperative coevolution to ascertain which coevolutionary approach is preferable for multiobjective optimization. The competitive coevolution will be implemented with K-Random Opponents strategy. The performances of SPEA2-CE and SPEA2-CC for solving tri-objective problems using the DTLZ suite of test problems are presented. The results show that the cooperative approach far outperforms the competitive approach when used to augment SPEA2 for tri-objective optimization in terms of all the metrics (generational distance, spacing and coverage).