Novel Search Scheme for Multi-Objective Evolutionary Algorithms to Obtain Well-Approximated and Widely Spread Pareto Solutions


In multi-objective optimization, the quality of Pareto-optimal solutions is evaluated by the efficiency of the optimal front (proximity), uniformity, and spread. This paper introduces a novel search scheme for multi-objective evolutionary algorithms (MOEAs), whose solutions demonstrate improved proximity and spread metrics. Our proposed scheme comprises two search phases with different search objectives. The first phase uses a reference-point-based approach to improve proximity; the second phase adopts a distributed-cooperation scheme (DC-scheme) to broaden the range of solutions. We experimentally investigate the effectiveness of our proposed scheme on the walking fish group (WFG) test suite of scalable multi-objective problems. Finally, we show the applicability of the proposed scheme to various types of MOEAs.