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题名:
Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization
作者: Shen H(申海); Zhu YL(朱云龙); Zhou XM(周小明); Guo HF(郭海峰); Chang CG(常春光)
作者部门: 先进制造技术研究室
会议名称: ACM/SIGEVO Summit on Genetic and Evolutionary Computation  
会议日期: June 12–14, 2009
会议地点: Shanghai, China
会议主办者: ACM
会议录: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
会议录出版者: ACM
会议录出版地: New York
出版日期: 2009
页码: 497-504
收录类别: EI ; CPCI(ISTP)
ISBN号: 978-1-60558-326-6
关键词: bacterial foraging ; numerical optimization ; particle swarm optimization
摘要: In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. However, during the process of chemotaxis, the BFOA depends on random search directions which may lead to delay in reaching the global solution. Recently, a new algorithm BFOA oriented by PSO termed BF-PSO has shown superior in proportional integral derivative controller tuning application. In order to examine the global search capability of BF-PSO, we evaluate the performance of BFOA and BF-PSO on 23 numerical benchmark functions. In BF-PSO, the search directions of tumble behavior for each bacterium oriented by the individual's best location and the global best location. The experimental results show that BF-PSO performs much better than BFOA for almost all test functions. That's approved that the BFOA oriented by PSO strategy improve its global optimization capability.
语种: 英语
产权排序: 1
EI收录号: 20093012219528
WOS记录号: WOS:000282382900067
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8163
Appears in Collections:工业信息学研究室_先进制造技术研究室_会议论文

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Recommended Citation:
Shen H,Zhu YL,Zhou XM,et al. Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization[C]. ACM/SIGEVO Summit on Genetic and Evolutionary Computation  . Shanghai, China. June 12–14, 2009.Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization.
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