一种改进粒子群优化算法

为了提高粒子群优化算法的性能，提出了一种惯性权值调整的改进粒子群优化算法，该算法的惯性权值满足不同。粒子对全局和局部搜索能力的不同需求，每次迭代后根据适应度值对惯性权值做相应的调整。对4个典型的测试函数进行仿真表明，该算法比标准粒子群优化算法有更好的收敛性和更快的收敛速度，改善了优化性能。

An Algorithm of Modified Particle Swarm Optimization

To enhance the performance of the particle swarm optimization,the individual inertia weight adjustment particle swarm optimization is proposed.Each particle has an individual inertia weight,which can provide the different global and local searching performances for particles.The inertia weight will be adjusted by the fitness of adaptive degree.Experimental results show that the new algorithm can greatly improve the global convergence ability and enhance the rate of convergence.