文献类型：期刊文献

英文题名：A Multipopulation-Based Multiobjective Evolutionary Algorithm

作者：Ma, Haiping[1];Fei, Minrui[2];Jiang, Zheheng[3];Li, Ling[4];Zhou, Huiyu[3];Crookes, Danny[5]

机构：[1]Shaoxing Univ, Dept Elect Engn, Shaoxing 312000, Peoples R China;[2]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China;[3]Univ Leicester, Dept Informat, Leicester LE1 7RH, Leics, England;[4]Univ Kent, Sch Comp, Gillingham ME4 4AG, Kent, England;[5]Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT3 9DT, Antrim, North Ireland

年份：2020

卷号：50

期号：2

起止页码：689

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：SCI-EXPANDED(收录号：WOS:000506849800024)、、EI(收录号：20184105927628)、Scopus(收录号：2-s2.0-85054522972)、WOS

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61640316 and Grant 61633016, and in part by the Fund for China Scholarship Council under Grant 201608330109. The work of H. Zhou was supported in part by U.K. EPSRC under Grant EP/N011074/1, and in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342.

语种：英文

外文关键词：Sociology; Statistics; Optimization; Mathematical model; Genetic algorithms; Evolutionary computation; Markov processes; Evolutionary algorithm; genetic algorithms (GAs); Markov chain; multiobjective optimization; multipopulation

外文摘要：Multipopulation is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multipopulation-based multiobjective genetic algorithm (MOGA) is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multipopulation MOGA is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multiobjective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multipopulation method can improve the optimization ability of the MOGA. Also, the proposed multipopulation method is applied to other multiobjective evolutionary algorithms (MOEAs) for evaluating its performance against the IEEE Congress on Evolutionary Computation multiobjective benchmarks. The experimental results show that a single-population MOEA can be extended to a multipopulation version, while obtaining better optimization performance.