HYBRID APPROACH FOR IMPROVED PARTICLE SWARM OPTIMIZATION USING ADAPTIVE PLAN SYSTEM WITH GENETIC ALGORITHM

被引：0

作者：

Pham Ngoc Hieu ^{[1
]}

Hasegawa, Hiroshi ^{[1
]}

机构：

[1] Shibaura Inst Technol, Dept Syst Engn & Sci, Saitama, Japan

来源：

ECTA 2011/FCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION THEORY AND APPLICATIONS AND INTERNATIONAL CONFERENCE ON FUZZY COMPUTATION THEORY AND APPLICATIONS | 2011年

关键词：

Particle swarm optimization (PSO); Genetic algorithms (GAs); Adaptive system; Multi-peak problems;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To reduce a large amount of calculation cost and to improve the convergence to the optimal solution for multi-peak optimization problems with multi-dimensions, we purpose a new method of Adaptive plan system with Genetic Algorithm (APGA). This is an approach for Improved Particle Swarm Optimization (PSO) using APGA. The hybrid strategy using APGA is introduced into PSO operator (H-PSOGA) to improve the convergence towards the optimal solution. The H-PSOGA is applied to some benchmark functions with 20 dimensions to evaluate its performance.

引用

页码：267 / 272

页数：6