改进粒子群优化算法在服务组合中的应用  

Application of Improved Particle Swarm Optimization Algorithm in Service Composition

作　　者：胡珀[1] 娄渊胜[1] 

机构地区：[1]河海大学计算机与信息学院,南京210098

出　　处：《计算机工程》2011年第17期130-132,共3页Computer Engineering

摘　　要：针对标准粒子群优化(PSO)算法存在收敛速度慢、容易陷入局部最优的问题,提出一个改进的PSO算法,该算法设计一种新的惯性权重,在粒子搜索的不同阶段采用不同的计算公式计算惯性权重,并引入自适应变异策略和线性变化的学习因子。实验结果表明,该算法的收敛性等性能比基本粒子群算法有明显提高,能较好地解决非线性问题。As the Particle Swarm Optimization（PSO） algorithm has some shortcomings of slow convergence and easy to fall into the local extreme value,this paper presents a improved particle swarm optimization with a new inertia weight.In different stages of the algorithm run,a corresponding formula is used to calculate the inertia weight.In Addition,adaptive mutation and linear-changed learning factor are introduced.The relational test simulation experiment is carried out.Experimental results show that the improved algorithm is feasible and efficient,it can solve norlinear problem.

关 键 词：粒子群优化 惯性权重 自适应变异 服务组合优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]