摘要
针对传统分布估计算法中存在的早熟收敛问题,在讨论种群多样性保持方法和早熟原因的基础上,提出一种多样性保持的分布估计算法(EDA-DP),具体措施包括:根据混沌运动具有的随机性、遍历性、初值敏感性和规律性等特点引入混沌变异算子;根据个体适应度值和种群中各个体之间的距离信息自适应调整变异半径;根据种群中的个体浓度信息生成子代种群.基准测试函数的实验结果表明,EDA-DP能够有效防止早熟收敛,在提高解的精度和加快收敛速度方面均有所改善.
In order to solve the premature convergence problem existing in the traditional estimation of distribution algorithm（EDA）,based on the analysis of methods for diversity preservation and reasons for premature convergence,an estimation of distribution algorithm with diversity preservation（EDA-DP）is proposed.A chaotic mutation operator is introduced into EDA by taking advantage of the randomness,ergodicity,initial value sensitivity and regularity of chaos.The EDA-DP is able to adjust its mutation radius in an adaptive way according to the fitness value and the distance between each individual.Moreover,the EDA-DP is able to generate its offspring population by making use of the concentration inside the population.The EDA-DP is evaluated on a set of benchmark problems and the experimental results show that the precision of the optimal solutions and the convergence speed are improved,thanks to the EDA-DP effectively overcomes the premature convergence problem.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2010年第3期591-597,共7页
Acta Electronica Sinica
基金
国家自然科学基金(No.60804022
60974050)
教育部新世纪优秀人才支持计划(No.NCET-08-0836)
江苏省自然科学基金(No.BK2008126)
高等学校博士学科点专项科研基金(No.20070290537
200802901506)
中国博士后科学基金特别资助(No.200902533)
关键词
分布估计算法
多样性
早熟收敛
混沌变异
estimation of distribution algorithm
diversity
premature convergence
chaotic mutation
