Multiobjective optimization based on reputation

To improve the robustness and ease-of-use of Evolutionary Algorithms (EAs), adaptation on evolutionary operators and control parameters shows significant advantages over fixed operators with default parameter settings. To date, many successful research efforts to adaptive EAs have been devoted to Single-objective Optimization Problems (SOPs), whereas, few studies have been conducted on Multiobjective Optimization Problems (MOPs). Directly inheriting the adaptation mechanisms of SOPs in the MOPs context faces challenges due to the intrinsic differences between these two kinds of problems. To fill in this gap, in this paper, a novel Multiobjective Evolutionary Algorithm (MOEA) based on reputation is proposed as a unified framework for general MOEAs. The reputation concept is introduced for the first time to measure the dynamic competency of evolutionary operators and control parameters across problems and stages of the search in MOEAs. Based on the notion of reputation, individual solutions then select highly reputable evolutionary operators and control parameters. Experimental studies on 58 benchmark MOPs in jMetal confirm its superior performance over the classical MOEAs and other adaptive MOEAs.