約束多目標優(yōu)化問題的雙群體差分進化算法-資料下載頁
2025-03-26 03:15本頁面
【正文】 710071, China；2. Institute of Intelligent Information Processing, Xidian University, Xi’an, 710071)Abstract: An improved differential evolution approach is given first, and a new algorithm based on double Populations for Constrained Multiobjective Optimization Problem (CMOP) is presented. In the proposed algorithm, two populations are adopted, one is for the feasible solutions found during the evolution, and the other is for infeasible solutions with better performance which are allowed to participate in the evolution with the advantage of avoiding difficulties such as constructing penalty function and deleting infeasible solutions directly. In addition, the time plexity of the proposed algorithm, NSGAⅡand SPEA are pared, which show the best is NSGAⅡ, followed by SPEA and our algorithm simultaneously. The experiments on benchmarks indicate that our algorithm is superior to NSGAⅡin the measure of GD and SP. Keywords: Differential Evolution。 Constrained Optimization Problem。 multiobjective optimization problem。BackgroundConstrained optimization, both for nonlinear programming and multiobjective optimization, is a very important problem and has a variety of applications in engineering, management, mathematics and other fields. A mon way to constrained optimization problem is to deal with constraints by penalty function, with the disadvantage and difficulty in choosing the penalty factors. In this paper, the authors propose a differential evolution based on double populations for constrained multiobjective optimization problem. By using the definitions and the mechanism in the paper, an effective evolutionary algorithm for constrained multiobjective optimization problem is given and the time plexity is discussed and pared with the state of the art—NSGAⅡand SPEA, which shows the time plexity of the proposed algorithm is as the same magnitude as SPEA and is higher than that of NSGAⅡ, while simulations illustrate that the proposed algorithm is superior to NSGAⅡin the measure of GD and SP. However, it is worthy to note that the time plexity of our algorithm will decrease greatly if a strategy like NSGAⅡis taken for updating optimal population.孟紅云，女， 1975年生，博士，副教授，主要研究領域為優(yōu)化理論與方法、自然計算、圖像處理. Email: mhyxdmath@. 張小華，男，1974年生，博士，副教授，主要研究領域為自然計算、智能信息處理、數(shù)據(jù)挖掘和數(shù)字水印. 劉三陽，男，1959年生，博士，教授，博士生導師，主要研究領域為優(yōu)化理論與方法.MENG Hongyun, born in 1975, Ph. D. , associate professor. Her research interests include Optimization theory and algorithm, natural putation, image processing.ZHANG Xiaohua, born in 1974, Ph. D. , associate professor. His research interests include natural putation, intelligent information processing, data mining, and digital watermark. LIU Sanyang, born in 1959, ., professor, . supervisor. His research interests include Optimization theory and methods.第一作者照片 作者孟紅云的聯(lián)系方式： Email: mhyxdmath@手機：013636804033 13 / 13
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