An Improved Particle Swarm Optimization Algorithm

被引：0
|
作者：
Jiang, Changyuan [1 ]
Zhao, Shuguang [1 ]
Guo, Lizheng [1 ]
Ji, Chuan [1 ]
机构：
[1] Donghua Univ, Coll Informat Sci & Technol, Shanghai 201600, Peoples R China
来源：
MECHANICAL ENGINEERING AND INTELLIGENT SYSTEMS, PTS 1 AND 2 | 2012年 / 195-196卷
关键词：
particle swarm optimization algorithm; inertia weight; sinusoidal changing; differential equation; CONVERGENCE;
D O I：
10.4028/www.scientific.net/AMM.195-196.1060
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.
引用
收藏
页码：1060 / 1065
页数：6
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