多目标优化与自适应惩罚的混合约束优化进化算法  

Multiobjective optimization and adaptive penalty function based constrained optimization evolutionary algorithm

作　　者：甘敏[1] 彭辉[1] 王勇[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083

出　　处：《控制与决策》2010年第3期378-382,共5页Control and Decision

基　　金：国家自然科学基金委创新群体项目(70921001);国家自然科学基金项目(60574058);湖南省科技厅国际合作重点项目(2009wk2009)

摘　　要：提出一种多目标优化与自适应惩罚函数相结合的方法来处理约束优化问题.首先利用多目标优化方法提取当前群体中的主要信息;然后进一步用自适应惩罚函数选出最有价值的信息.将这种约束处理技术与一种基于群的算法生成器模型相结合,即可得到一种新的约束优化进化算法.选取10个标准测试函数对新算法的性能进行数值实验,结果表明了所提出方法的有效性和较强的稳健性,与其他尖端算法相比得到了相似或更优的结果.A hybrid constraint-hadiling method based on multiobjective optimization and adaptive penalty function is presented for constrained evolutionary optimization. The main idea of this approach is to take advantage of multiobjective optimization techniques to extract the main information contained in the current population,and further selcet the most valuable information by using the penalty function to direct the population to evlove. By combining this constraint-handling technique with a model of a population-based algorithm-generator,a novel constrained optimization evolutionary algorithm is derived. The results of experiment on 10 benchmark test functions verify the effectiveness of the proposed method. The results show that the new approach is robust and stable,and achieves competitive performance with respect to other state-of-the-art approaches.

关 键 词：多目标优化 自适应惩罚 约束优化问题 进化算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]