保持粒子活性的改进粒子群优化算法

Improving Particle Swarm Optimization by keeping particles activity

针对基本粒子群优化算法(Particle Swarm Optimization,简称PSO)存在的早熟收敛问题,提出了一种保持粒子活性的改进粒子群优化(IPSO)算法。当粒子失活时,对粒子进行变异或扰动操作,重新激活粒子,使粒子能够有效地进行全局和局部搜索。通过对4种Benchmark函数的测试,结果表明IPSO算法不仅具有较快的收敛速度,而且能够更有效地进行全局搜索。

To overcome the problem of premature convergence on Particle Swarm Optimization（PSO）,this paper proposes an Improved Particle Swarm Optimization（IPSO） called keeping particles active PSO ,which is guaranteed to keep the diversity of the particle swarm.When particles lose activity,this paper uses a special mutation or perturbation to activate particles and to make particles explore the search space more efficiently.Four Benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,but also effectively Solve the premature convergence problem.