A CGS-MSM Parallel Genetic Algorithm Based on Multi-agent

Author

Zhao, TingHong ; Man, Zibin ; Wan, Zhijun ; Bi, Guiquan

Author_Institution

Coll. of Fluid Power & Control Eng., Lanzhou Univ. of Technol., Lanzhou

fYear

2008

fDate

25-26 Sept. 2008

Firstpage

10

Lastpage

13

Abstract

In order to solve problem of the speed of convergence and early-maturing of the serial genetic algorithm, people begin to study the parallel genetic algorithm. However most researcher devoted to study the single way parallel genetic algorithm (PGA), while solving some combination optimizing problem, have made good result, but as to the more complicated combination optimizing problem, all have certain deficiencies. This paper propose a coarse grain size-master slaver model PGA (CGS-MSM PGA), which is make up of several coarse grain size PGA (CGS-PGA) that is combine of several master-slaver model parallel genetic algorithm (MSM-PGA), and every sub-algorithm is one agent. This algorithm has improved the speed and precision of calculation greatly through the good communication coordination among many agents, utilized the advantages of CGS-PGA and MSM-PGA, overcome the shortcoming of their two.

Keywords

genetic algorithms; multi-agent systems; parallel algorithms; CGS-MSM parallel genetic algorithm; coarse grain size-master slaver model; combination optimizing problem; multiagent system; Concurrent computing; Electronics packaging; Genetic algorithms; Grain size; Intelligent agent; Master-slave; Multiagent systems; Optimization methods; Power engineering computing; Search methods; CGS-MSM PGA; Multi-Agent; manager Agent;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolutionary Computing, 2008. WGEC '08. Second International Conference on

Conference_Location

Hubei

Print_ISBN

978-0-7695-3334-6

Type

conf

DOI

10.1109/WGEC.2008.57

Filename

4637384