INVESTIGATING MULTIOBJECTIVE OPTIMIZATION USING AN AUGMENTED COEVOLUTIONARY SPEA2 ALGORITHM

Abstract

A new algorithm for solving multiobjective optimization problems with three to five objectives, namely SPEA2-CE-KR, is proposed. This introduced algorithm is an extension of a state of the art multiobjective evolutionary algorithm: Strength Pareto Evolutionary Algorithm 2 (SPEA2). SPEA2-CE-KR is a hybridization of SPEA2 and Competitive Coevolution (CE) concept with K-Random Opponents (KR) competitive fitness strategy. Comparison performance between SPEA2-CE-KR and SPEA2 are demonstrated with a set of DTLZ test problems, from DTLZ1 to DTLZ7. The experimental results reveal that, this presented algorithm outperforms SPEA2 in terms of convergence to the true Pareto front and the coverage of the obtained nondominated solutions.