一种新的自适应惯性权重混沌PSO算法  

New chaos particle swarm optimization based on adaptive inertia weight

作　　者：李龙澍[1,2] 张效见 LI Longshu;ZHANG Xiaojian(Key Laboratory of Intelligent Computing and Signal Processing,Anhui University,Hefei 230039,China;School of Computer Science and Technology,Anhui University,Hefei 230601,China)

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,合肥230039 [2]安徽大学计算机科学与技术学院,合肥230601

出　　处：《计算机工程与应用》2018年第9期139-144,共6页Computer Engineering and Applications

基　　金：青年科学基金项目(No.61402005)

摘　　要：针对粒子群算法(Particle Swarm Optimization,PSO)易陷入局部极值的缺陷,提出了一种新的自适应惯性权重混沌PSO算法(a New Chaos Particle Swarm Optimization based on Adaptive Inertia Weight,CPSO-NAIW)。首先采用新的惯性权重自适应方法,很好地平衡粒子的搜索行为,减少算法陷入局部极值的概率,然后在算法陷入局部极值时,引入混沌优化策略,对群体极值位置进行调整,以使粒子搜索新的邻域和路径,增加算法摆脱局部极值的可能。最后,实验结果表明,CPSO-NAIW算法能有效避免陷入局部极值,提高算法性能。Particle Swarm Optimization(PSO)is easy to fall into the local optimal value.According to this disadvantage,a New Chaos Particle Swarm Optimization based on Adaptive Inertia Weight(CPSO-NAIW)is proposed.Firstly,the new inertia weight adaptive method is used to make a balance between the global and local search of the particles.It can reduce the probability of particles trap in local optimal.Then,when the algorithm falls into local optimal value,the strategy of chaos optimization is introduced to adjust the position of the population’s extreme value so that the particles can search the new neighorhood and path.The probability of getting rid of the local extremum is increaseed.Finally,the experimental results show that the CPSO-NAIW algorithm can avoid the local optimal and improve the performance of the algorithm effectively.

关 键 词：粒子群 自适应惯性权重 混沌 局部极值 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]