一种改进的粒子群优化算法及其仿真

粒子群优化算法(particle swarm optimization,PSO)是一种新兴的优化技术,其思想来源于人工生命和演化计算理论。PSO算法具有简单、易实现、可调参数少等特点,在很多领域得到了广泛应用。但PSO算法存在早熟收敛问题。为了克服粒子群优化算法的早熟收敛问题,提出了一种旨在保持种群多样性的改进PSO(IPSO)算法,以提高PSO算法摆脱局部极小点的能力。通过对3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、有效的全局收敛性能,而且还具有良好的稳定性。

An Improved Particle Swarm Optimization and Simulation

Particle swarm optimization(PSO) algorithm is a new optimization technique originating from artificial life and evolutionary computation.PSO is easily understood,realized.PSO has few parameters need to be tuned,and has been applied widely.To overcome the problem of premature convergence on PSO,proposes an improved particle swarm optimization(IPSO),which is guaranteed to keep the diversity of the particle swarm and to improve performance of basic PSO algorithm.Three benchmark functions are selected as the test functions.The experimental results show that the IPSO can not only significantly speed up the convergence,effectively solve the premature convergence problem,but also have good stability.