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基于互补变异算子的自适应差分进化算法
Self-adaptive differential evolution algorithms based on complementary mutation operators
【Author】 Xin Bin Chen Jie Peng Zhihong Dou Lihua(School of Automation,Beijing Institute of Technology,Beijing 100081,China)(Key Laboratory of Complex System Intelligent Control and Decision of Ministry of Education,Beijing Institute of Technology,Beijing 100081,China)
【机构】 北京理工大学自动化学院; 北京理工大学教育部复杂系统智能控制与决策重点实验室;
【摘要】 在参数自适应的差分进化算法的基础上,同时采用DE/rand/1和DE/best/2两种具有互补特性的差分变异算子,提出了多种采用不同分配策略的新型差分变异算法.2种变异算子的分配分别采用随机分配、基于种群规模的单调分配、适应性随机分配以及基于种群规模的适应性分配4种策略.基于标准测试函数的数值优化结果表明:双变异模式的自适应差分进化算法总体上明显优于2种标准DE算法.在4种分配策略中,单调分配策略效果最佳.所提出的DE算法利用了DE/rand/1型变异在保持种群多样性方面的优势,并继承了DE/best/2型变异局部收敛速度快的优点,较好地实现了探索与利用的平衡,而且需要人工调节的参数较少,便于在实际中使用.
【Abstract】 Based on the differential evolution(DE) algorithm with self-adaptive parameters,several novel DE algorithms are proposed.These algorithms adopt both DE/rand/1 and DE/best/2 mutation operators which are of complementary virtue,and adjust the use of two mutation operators by multiple different assignment strategies including random assignment,monotone assignment based on population size,adaptive random assignment and adaptive assignment based on population size.The results of numerical optimization based on benchmark test functions show that the self-adaptive DE algorithms with two mutation operators are obviously better than two canonical DE algorithms.Among the four assignment strategies,the monotone assignment has the best effect.The proposed DE algorithms take advantage of the DE/rand/1 mutation in preserving population diversity,inherit the virtue of fast local convergence from the DE/best/2 mutation and achieve a better tradeoff between exploration and exploitation.Moreover,in these new DE algorithms few parameters need to be tuned manually,so it is convenient to use them in practice.
【Key words】 differential evolution; self-adaptation; differential mutation; numerical optimization;
- 【会议录名称】 2009年中国智能自动化会议论文集(第五分册)[东南大学学报(增刊)]
- 【会议名称】2009年中国智能自动化会议
- 【会议时间】2009-09-27
- 【会议地点】中国江苏南京
- 【分类号】TP301.6
- 【主办单位】中国自动化学会智能自动化专业委员会、江苏省自动化学会