期刊
IEEE TRANSACTIONS ON MAGNETICS
卷 44, 期 6, 页码 1078-1081出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMAG.2007.916027
关键词
chaotic sequences; electromagnetic optimization; genetic algorithms; multiobjective optimization; TEAM 22 benchmark
Real-world engineering optimization problems involve multiple design factors and constraints and consist in minimizing multiple noncommensurable and often competing objectives. In recent years, many evolutionary techniques for multiobjective optimization have been proposed. In this context, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm is an effective methodology to solve multiobjective optimization problems. A modified NSGA-II to seek the Pareto front of electromagnetic multiobjective design problems is proposed in this paper. We propose the use of chaotic sequences based on Zaslavskii map in the NSGA-II crossover operator. The proposed method is tested on TEAM 22 benchmark optimization problem with promising results.
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