Cooperative versus competitive coevolution for Pareto multiobjective optimization

Authors:
Tse Guan Tan;Hui Keng Lau;Jason Teo
Affiliations:
Center for Artificial Intelligence, School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia;Center for Artificial Intelligence, School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia;Center for Artificial Intelligence, School of Engineering and Information Technology, Universiti Malaysia Sabah, Malaysia
Venue:
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Year:
2007

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Abstract

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 triobjective 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).