Self-Adaptation in Bacterial Foraging Optimization Algorithm

摘要：

Bacterial Foraging Optimization(BFO)is a recently developed nature-inspired optimization algorithm,which is based on the foraging behavior of E.coli bacteria.However,BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like Genetic Algorithm(GA) and Particle Swarm Optimization(PSO).This paperfirst analyzes how the run-length unit parameter controls the exploration and exploitation abilily of BFO,and then presents a variation on the originalBFO algorithm,called the Self-adaptive BacterialForaging Optimization(SA-BFO),employing theadaptive search strategy to significantly improve theperformance of the original algorithm.This isachieved by enabling SA-BFO to adjust the run-lengthunit parameter dynamically during evolution tobalance the exploration/exploitation tradeoff.Application of SA-BFO on several benchmarkfunctions shows a marked improvement inperformance over the original BFO.

作者: Hanning Chen Yunlong Zhu Kunyuan Hu

作者单位: Key Laboratory of Industrial Informatics,Shenyang Institute of Automation,Chinese Academy of Science Key Laboratory of Industrial Informatics,Shenyang Institute of Automation,Chinese Academy of Science

会议类型: 国际会议

会议名称: 2008 3rd International Conference on Intelligent System and Knowledge Engineering(第三届智能系统与知识工程国际会议)(ISKE 2008)

会议地点: 厦门

会议语种:英文

页码: 1026-1031

在线出版日期: 2008-11-17（万方平台首次上网日期，不代表论文的发表时间）