Competitive Coevolution with K-Random Opponents for Pareto Multiobjective Optimization
Tse Guan Tan, Jason Teo, HuiKeng Lau · 2007
In this paper, our objective is to conduct comprehensive tests for competitive coevolution using an evolutionary multiobjective algorithm for 3 dimensional problems. This competitive coevolution will be implemented with k-random opponents strategy. A new algorithm which integrates competitive coevolution (CE) and the strength Pareto evolutionary algorithm 2 (SPEA2) is proposed to achieve this objective. The resulting algorithm is referred to as the strength Pareto evolutionary algorithm 2 with competitive coevolution (SPEA2-CE). The performance between SPEA2-CE is compared against SPEA2 to solve problems with each having three objectives using DTLZ suite of test problems. In general, the results show that the SPEA2-CE with k- random opponents performed well for the generational distance and coverage but performed less favorably for spacing.