[1]AN Xiao-wei,SU Hong-sheng.An Improved Group Search Optimization Algorithm[J].Journal of Zhengzhou University (Engineering Science),2015,36(02):105-109.[doi:10.3969/ j. issn.1671 -6833.2015.02.023]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
36卷
Number of periods:
2015 02
Page number:
105-109
Column:
Public date:
2015-04-30
- Title:
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An Improved Group Search Optimization Algorithm
- Author(s):
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AN Xiao-wei1; SU Hong-sheng1; 2
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1.Sehool of Automation and Electrical Engineering,Lanzhou Jiaotong University ,Lanzhou 730070,China; 2.School of Auto-mation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
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- Keywords:
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group searching optimization; function optimization; artificial fish swarm algorithm
- CLC:
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TM301.6
- DOI:
-
10.3969/ j. issn.1671 -6833.2015.02.023
- Abstract:
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Group Search Optimization (GSO) is a swarm intelligence approach inspired by animal searchingbehavior and group living theory. It is simple and efficient , and easy to implement. The searching mode of thescrounger is oversimplified , so it falls into local optimum easily. In order to enhance its convergence speed andprecision,the improved Group Search Optimization ( IGS0) is proposed. Inheriting the strategy of producer-scrounger of GSO,IGSO introduces the strategy of the Antificial Fish Swarm (AFS) algorithm to the behaviorof the scrounger. By introducing prey , fellow , swarm and leap of the AFS algorithm , searching forms is diver-sified,as well as the best individuals of group and best groups of population can be considered,IGS0 can ef-ectively avoid the local optimum. Six benchmark functions are used to evaluate the performance of two algo-rithms..Experimental results.show that ICSO.is able to achieve better results than standard GSo.