›› 2012, Vol. ›› Issue (06): 1491-1497.

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Optimization algorithm of bacterial swarm based on the collection

XU Xin1, LIU Yan-heng1,2, WANG Ai-min1,2, CHEN Hui-ling1,3, SUN Xin1   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. Symbol Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3. College of Physics and Electronic Information, Wenzhou University, Wenzhou 325035, China
  • Received:2012-01-20 Online:2012-11-01

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Abstract: The conventional Bacterial Foraging Optimizer (BFO) is easy to get stuck in local minima in solving high dimensional optimization problems. This paper proposes an adaptive Bacterial Swarm Optimizer (BSO) with time-varying acceleration coefficients to solve the local-optimal problems of BFO. The proposed method, termed as ABSO-TVAC, is applied to optimize Combinatorial Optimization Problems (COPs). When dealing with COPs, according to the cell-to-cell signaling in E. coli swarm, a conversion rule between continuous space and discrete one is designed. All arithmetic operators in the velocity and position updating rules used in the ABSO-TVAC are described in a manner of set. Simulation shows that the proposed algorithm has the capability to avoid the premature problem, and it is superior to Ant Colony Optimization (ACO) and comparable to set-based particle swarm optimization.

Key words: artificial intelligence, combinatorial optimization problems, discrete space, bacterial foraging optimizer, optimization algorithm of bacterial swarm

CLC Number: 

  • TP18
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