基于余弦函数改进的PSO算法及其仿真  

Improved PSO algorithm based on cosine functions and its simulation

作　　者：张敏[1] 黄强[1] 许周钊[2] 姜柏庄[1] 

机构地区：[1]湖南科技大学信息与电气工程学院,湖南湘潭411201 [2]湖南省电力公司岳阳汨罗电力局,湖南岳阳414400

出　　处：《计算机应用》2013年第2期319-322,共4页journal of Computer Applications

基　　金：湖南省教育厅资助项目(09A025);湖南科技大学创新基金资助项目(S110114)

摘　　要：粒子群算法具有简单、易于实现等优点在科学与工程领域得到了很好的验证,但是粒子群优化算法与其他进化算法一样存在容易陷入局部极小和早熟收敛等缺点。分析了其存在缺点的主要原因,并此基础上提出了一种改进的粒子群算法(CPSO)。利用余弦函数非线性改变惯性权重、对称改变学习因子进一步提高了粒子的学习能力,同时引入了细菌趋化操作用以维持种群多样性,使得CPSO算法性能在一定程度上优于标准粒子群(SPSO)算法。利用五个标准测试函数对三种算法的仿真结果进行可对比分析,分析结果表明:CPSO算法能在一定程度上跳出局部最优,有效地避免了SPSO算法早熟收敛问题,并具有较快的收敛速度。The advantages of simplicity and easy implementation of Particle Swarm Optimization （PSO） algorithm have been validated in science and engineering fields. However, the weaknesses of PSO algorithm are the same as that of other evolutionary algorithms, such as being easy to fall into local minimum, premature convergence. The causes of these disadvantages were analyzed, and an improved algorithm named Cosine PSO （CPSO） was proposed, in which the inertia weight of the particle was nonlinearly adjusted based on cosine functions and the learning factor was symmetrically changed, as well as population diversity was maintained based on bacterial chemotaxis. Therefore, CPSO algorithm is better than the Standard PSO （SPSO） in a certain degree. Simulation comparison of the three algorithms on five standard test functions indicates that, CPSO algorithm not only jumps out of local optimum and effectively alleviates the problem of premature convergence, but also has fast convergence speed.

关 键 词：粒子群优化 惯性权重 学习因子 细菌趋化 种群多样性 早熟收敛 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]