改进粒子群优化算法在服务组合中的应用

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。

Application of Improved Particle Swarm Optimization Algorithm in Service Composition

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 alg...