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基于精英选择和个体迁移的多约束多目标问题求解

发表时间:2013-02-28  浏览量:1059  下载量:459
全部作者: 祁荣宾,钱锋
作者单位: 华东理工大学化工过程先进控制和优化技术教育部重点实验室
摘 要: 针对以往多约束优化问题对约束条件处理存在的缺陷,提出了新的处理多约束的进化算法,它采用一个约束适应度函数量化一个解满足约束条件的情况,以便更精确地比较不同解的优劣性。该方法既考虑了个体满足约束条件数量的差异,也兼顾了打破约束程度的刻画。研究根据约束适应度函数重新定义多目标优化问题的Pareto可控性和Pareto最优解,通过基于精英选择和个体迁移的多目标进化算法实现了多约束多目标优化问题的求解。仿真实验表明:提出的方法能够较好地收敛到Pareto前沿解,并且解的分布均匀一致,在求解一类多约束多目标优化问题上表现出一定的优势。
关 键 词: 控制理论;约束程度量化;多约束多目标;Pareto可控性;进化算法
Title: Solving for multi-constrained and multi-objective problems based on elitist selection and individual migration
Author: QI Rongbin, QIAN Feng
Organization: Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology
Abstract: According to the existed limitation of multi-constrained optimization to multi-constrained processes, a new evolutionary algorithm for multi-constraint handing problem was proposed in this paper. Constrained fitness function was applied to quantify the extent of the constraint violation for comparing the advantages and disadvantages of different solutions. This method took both the number of violated constraints and the amount of constraint violation into account in order to measure the quality of individual solutions in more accurate way. Definitions of Pareto domination and Pareto optimal solutions were renewed according to the constrained fitness function. And an evolutionary algorithm based on elitist selection and individual migration was applied to solving the multi-objective and multi-constrained problems. Simulation experiments indicated that the proposed method could converge to Pareto frontier solutions better, and the spread of solutions was uniformly. In addition, the proposed method has superiority over the multi-objective and multi-constrained problems.
Key words: control theory; quantify of constraint condition; multi-constraint and multi-objective; Pareto domination; evolutionary algorithm
发表期数: 2013年2月第4期
引用格式: 祁荣宾,钱锋. 基于精英选择和个体迁移的多约束多目标问题求解[J]. 中国科技论文在线精品论文,2013,6(4):349-356.
 
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