An Improved Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm

摘要：

Based on the study and analysis of A Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm (RM-MEDA), we propose An Improved Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm (IRM-MEDA). The IRM-MEDA had some features. 1) generate initial population with orthogonal design so that the individuals make a more representative distribution of the feasible solutions. 2)introduce a new convergence criterion to determine when the genetics-based method, i.e. crossover, mutation and when the EDA-based method should be used to generate offspring.3)combine genetics-based and model-based offspring generation instead of only model-based method in RM-MEDA. The experiment result on a number of test problems proved that An Improved Regularity Model-Based Multi-Objective Estimation of Distribution Algorithm is able to find much better convergence near the the true Pareto-optimal solutions and better spread of solutions than RM-MEDA.

作者: Jianwen Wang Dai Guangming Wei Zheng

作者单位: School of Computer, China University of Geosciences, uhan,China School of Computer, China University of Geosciences, Wuhan 430074, China

会议类型: 国际会议

会议名称: Third International Symposium on Intelligence Computation and Applications(ISICA 2008)(第三届智能自动化、计算与制造国际研讨会)

会议地点: 武汉

会议语种:英文

页码: 66-71

在线出版日期: 2008-12-19（万方平台首次上网日期，不代表论文的发表时间）