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Self-Adaptive Bacterial Foraging Algorithm Based on Particle Swarm Optimization Strategy(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2013年01期
Page:
50-
Research Field:
Publishing date:

Info

Title:
Self-Adaptive Bacterial Foraging Algorithm Based on Particle Swarm Optimization Strategy
Author(s):
Zhao Chunli; Liu Qing
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210023,China
Keywords:
BFA algorithm; convergence rate; self-adaptive chemotactic step; PSO algorithm
PACS:
TP301
DOI:
-
Abstract:
To overcome the problems of low convergence rate,and poor convergence characteristics for larger constrained problems in conventional bacterial foraging algorithm(BFA),this paper proposes a new self-adaptive algorithm bacterial foraging based on PSO(ABF-PSO).The new algorithm improves the search ability with self-adaptive chemotactic steps,and controls the bacterial movement directions according to particle swarm optimization strategy,thus avoiding a delay in reaching the global solution because of random selection of the bacterial movement directions.After the detailed illustrations of dynamic adjustment bacterial chemotactic step,and updating bacterial movement directions by the velocity formula of PSO,this paper tests some classical functions with PSO algorithm,BFA algorithm,and ABF-PSO algorithm.The results show that ABF-PSO algorithm not only has greater improvement in convergence rate,but also gets a fruitful achievement in searching complex and high-dimensioned problems.

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Last Update: 2013-03-31