A Multi-objective Constrained Optimization Algorithm Based on Infeasible Individual Stochastic Binary-Modification

摘要：

During solving the constrained multi-objective optimization problems with evolutionary algorithms, constraint handling is a principal problem. Analyzing the existing constraint handling methods, a novel constraint handling strategy based on infeasible individual stochastic binary-modification is proposed in the paper. Its key point lies in modifying randomly infeasible individual into feasible one according to predefined modification rate (Rm) during evolutionary optimization. Finally, the proposed strategy is applied to the constrained multiobjective optimization evolutionary algorithm, and then the algorithm is tested on 7 benchmark problems and the comparison between our strategy and Debs Constrained-Domination principle demonstrates that our strategy optimizes 30％ faster than Debs in the circumstances to preserve equivalent distribution and convergence of the solutions found.

关键词： Evolutionary Multi-objective Optimization Constraint Handling Stochastic Binary-Modification

作者: GENG Huan-Tong Song Qing-Xi Wu Ting-Ting Liu Jing-Fa

作者单位: College of Computer & Software,Nanjing University of Information Science & Technology Nanjing,China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(2009 IEEE 智能计算与智能系统国际会议)

会议地点: 上海

会议语种:英文

页码: 89-93

在线出版日期: 2009-11-20（万方平台首次上网日期，不代表论文的发表时间）