An improved particle swarm optimisation based on cellular automata

被引：13

作者：

Dai, Yuntao ^{[1
]}

Liu, Liqiang ^{[2
]}

Li, Ying ^{[3
]}

Song, Jingyi ^{[4
]}

机构：

[1] Harbin Engn Univ, Dept Sci, 145 NanTong St, Harbin, Heilongjiang, Peoples R China

[2] Harbin Engn Univ, Dept Automat, Harbin, Heilongjiang, Peoples R China

[3] Anhui Sun Create Elect Co Ltd, Dept Image Proc, New & High Technol Dev Dist, Hefei, Anhui, Peoples R China

[4] Harbin Engn Univ, Dept Sci, Harbin, Heilongjiang, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS | 2014年 / 5卷 / 01期

基金：

中国国家自然科学基金;

关键词：

particle swarm optimisation algorithm; cellular automata; function optimisation;

D O I：

10.1504/IJCSM.2014.059385

中图分类号：

T [工业技术];

学科分类号：

08 [工学];

摘要：

Particle swarm optimisation (PSO) algorithm is easy to fall into local optimum, so an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of particle swarm is considered as cellular automata, and is distributed in two-dimensional grid. The state update of each cell is not only related to its own state and the neighbour state, but also related with the state of the optimal cell. If the state is too close with the optimal cell, then the cell state is re-update. Simulation experiments on typical test functions show that, compared with other algorithms, the proposed algorithm has good robustness, strong local search ability and global optimisation ability, and can solve the optimisation problems effectively.

引用

页码：94 / 106

页数：13

共 26 条

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