计算机工程与应用Issue(16)：35-38,47,5.DOI:10.3778/j.issn.1002-8331.1208-0478
带过滤机制非线性惯性权重粒子群算法
Nonlinear inertia weight particle swarm optimization with filtering mechanism
秦毅 1彭力1
作者信息
- 1. 江南大学 物联网工程学院，江苏 无锡 214122
- 折叠
摘要
Abstract
This paper proposes nonlinear inertia weight particle swarm optimization with a filtering mechanism to improve the non-linear inertia weight particle swarm algorithm. Due to the original algorithm exsists two shortcomings of particles falling into the local optimal solution and lower search efficiency, introduces fitness scaling function to the nonlinear inertia dynamically for the particle swarm optimization, fitness of excellent and poor particle are removed, then copy some excellent individual of remaining population, meanwhile randomly generated new particles, and crossover operation to them, popu-lations remain unchanged, the methed reduces the opportunity that particulates fall into the localmaximum and make the results converge to the global optimum. In order to verify the effectiveness of the algorithm. In this paper, low dimensions and high dimensional function are compared with each other. The result shows that this method achieves good effects.关键词
过滤机制/适应度缩放/惯性权重/非线性粒子群算法Key words
filtering mechanism/fitness scaling/inertia weight/nonlinear particle swarm algorithm分类
信息技术与安全科学引用本文复制引用
秦毅,彭力..带过滤机制非线性惯性权重粒子群算法[J].计算机工程与应用,2014,(16):35-38,47,5.
