| 摘要: |
| 针对基本粒子群优化(basic particle swarm optimization,简称bPSO)算法容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,采用简化粒子群优化方程和添加极值扰动算子两种策略加以改进,提出了简化粒子群优化(simple particle swarm optimization,简称sPSO)算法、带极值扰动粒子群优化(extremum disturbed particle swarm optimization,简称tPSO)算法和基于二者的带极值扰动的简化粒子群优化(ext |
| 关键词: 进化计算 群体智能 粒子群优化 极值扰动 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| A Simpler and More Effective Particle Swarm Optimization Algorithm |
|
HU Wang,LI Zhi-Shu
|
| Abstract: |
| The basic particle swarm optimization (bPSO) has some demerits, such as relapsing into local extremum, slow convergence velocity and low convergence precision in the late evolutionary. Three algorithms, based on the simple evolutionary equations and the extrenum disturbed arithmetic operators, are proposed to overcome the demerits of the bPSO. The simple PSO (sPSO) discards the particle velocity and reduces the bPSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. The extremum disturbed PSO (tPSO) accelerates the particles to overstep the local extremum. The experiment results of some classic benchmark functions show that the sPSO improves extraordinarily the convergence velocity and precision in the evolutionary optimization, and the tPSO can effectively break away from the local extremum. tsPSO, combined the sPSO and tPSO, can obtain the splendiferous optimization results with smaller population size and evolution generations. The algorithms improve the practicality of the particle swarm optimization. |
| Key words: evolutionary computation swarm intelligence particle swarm optimization disturbed extremum |
