期刊
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
卷 41, 期 2, 页码 553-567出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2010.2068046
关键词
Cultural algorithm; multiobjective optimization; particle swarm optimization
Multiobjective particle swarm optimization (MOPSO) algorithms have been widely used to solve multiobjective optimization problems. Most MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the personalized flight parameters of the mutated particles in a MOPSO, namely momentum and personal and global accelerations, for each individual particle based upon various types of knowledge in belief space, specifically situational, normative, and topographical knowledge. A comprehensive comparison of the proposed algorithm with chosen state-of-the-art MOPSOs on benchmark test functions shows that the movement of the individual particle using the adapted parameters assists the MOPSO to perform efficiently and effectively in exploring solutions close to the true Pareto front while exploiting a local search to attain diverse solutions.
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