计算机工程与应用2017，Vol.53Issue(5)：57-63,7.DOI:10.3778/j.issn.1002-8331.1608-0018
基于CAS理论的改进PSO算法
Improved particle swarm algorithm based on theory of complex adaptive syste m
摘要
Abstract
In order to solve the shortcomings that particle swarm optimization algorithm is easy to fall into local optimum and form early-maturing, this paper proposes a new Dual Adaptive PSO algorithm(DAPSO)which based on the theory of complex adaptive system by introducing the concept of chaos and adaptivity. Firstly, it uses the Logistic equation to create chaotic sequence in the beginning of initializing population. Secondly, it uses nonlinear dynamic adjustment strategy to ad-just the particle's individual learning factor and social learning factor. Thirdly, it uses(0, 1)random uniform distribution to instead of decreasing inertia weight to adjust inertia weight w. Finally, it uses six high-dimensional single mode and multi-modal Benchmark test function to do a simulation and it makes a comparison with PSO, 2PSO and KPSO. The re-sult shows that DAPSO algorithm is more effective than the original particle swarm optimization algorithm in solving the global optimal and it has a better performance on the accuracy and the efficiency than other algorithms.关键词
复杂适应系统(CAS)理论/双重自适应粒子群优化(DAPSO)算法/Logisitic方程/非线性动态调整策略/(0,1)随机均匀分布Key words
theory of complex adaptive system/Dual Adaptive Particle Swarm Optimization(DAPSO)algorithm/Logistic equation/nonlinear dynamic adjustment strategy/(0/1)random uniform distribution分类
信息技术与安全科学引用本文复制引用
刘举胜,何建佳,李鹏飞..基于CAS理论的改进PSO算法[J].计算机工程与应用,2017,53(5):57-63,7.基金项目
国家自然科学基金(No.71171135) （No.71171135）
上海市一流学科建设项目(No.S1201YLXK) （No.S1201YLXK）
上海市高原学科(管理科学与工程)建设项目 （管理科学与工程）
上海高校青年教师培养资助计划项目(No.slg14020) （No.slg14020）
上海理工大学国家级项目培育课(No.15HJPY-QN09) （No.15HJPY-QN09）
上海市哲学社会科学规划课题(No.2016EGL007). （No.2016EGL007）
