Fuzzy Penalty Function Approach for Constrained Function Optimization with Evolutionary Algorithms

摘要：

In this paper, a novel fuzzy penalty Junction approach is proposed for solving the constrained optimization problems using evolutionary algorithms. The fuzzy penalty is constructed according to the information contained In individuals so that it can be tuned to reflect the appropriate penalty need for better search. Simulations on ten case studies indicate that the proposed method is very effective.

作者: Baolin Wu Xinghuo Yu Li Liu

作者单位: Faculty of Informatics and Communication,Central Queensland University Rockhampton Old 4701, Austral School of Computing and Mathematics,Deakin University Geelong, Victoria 3217, Australia

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 337-342

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）