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

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.