Research on Improved Particle-Swarm-Optimization Algorithm based on Ant-Colony-Optimization Algorithm

被引：0
|
作者：
Li, Dong [1 ,2 ,3 ]
Shi, Huaitao [1 ,2 ]
Liu, Jianchang [3 ]
Tan, Shubin [3 ]
Li, Chi [4 ]
Xie, Yu [5 ]
机构：
[1] Shenyang Jianzhu Univ, Sch Mech Engn, Shenyang 110168, Liaoning, Peoples R China
[2] Shenyang Jianzhu Univ, National Local Joint Engn Lab NC Machining Equipm, Shenyang 110168, Liaoning, Peoples R China
[3] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Liaoning, Peoples R China
[4] Northern Heavy Ind Grp Co Ltd, Shenyang 110141, Liaoning, Peoples R China
[5] Shenyang FIDIA CNC Machine Tool Co Ltd, Shenyang 110000, Liaoning, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
关键词：
Optimization; Particle Swarm; Ant Colony System;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In order to alleviate Linearly Decreasing Weight of Particle Swarm Optimization (LDW-PSO) algorithm falling into the local optimum, Particle Swarm Optimization combined with Ant Colony Optimization (PSO-ACO) algorithm is designed. A pseudo-random-proportional rule is introduced to the determination of the swarm optimum value in PSO for improving the swarm diversity. The calculation expression of particle positions is improved in combination with the calculation expression of the pheromone concentration, which makes particles pay more attention to the current search information and accelerate the search speed. The simulation experiment results show that PSO-ACO has higher convergence accuracy and satisfactory solution speed in the solution of several typical test-functions.
引用
收藏
页码：853 / 858
页数：6
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