【摘要】 本文在细菌趋药性(BacterialChemotaxis,BC)优化算法基础上提出一种基于群体智能的函数优化方法-细菌群体趋药性(BacterialColonyChemotaxis,BCC)算法。细菌群体趋药性算法同时使用单个细菌在引诱剂环境下的应激反应动作和细菌群体间的位置信息交互来进行函数优化。细菌群体趋药性算法在保留单个细菌较强的搜索能力的基础上克服了细菌趋药性算法收敛速度较慢,性能难以与其他常用的智能优化算法比较的不足。对不同函数优化试例的仿真表明细菌群体趋药性算法性能良好,是一种具有进一步研究价值的集群函数优化方法。更多还原

【Abstract】 We present a new kind of collective intelligent function optimization method, Bacterial Colony Chemotaxis (BCC) algorithm, based on Bacterial Chemotaxis (BC) algorithm. BCC algorithm takes advantage of both a single bacterium’s reaction to chemoattractants and the exchange of position information among bacteria to find the optimum. BCC algorithm much improves the performance of BC algorithm while possessing the searching ability of a single bacterium, making it comparable to many other well-used intelligent optimization methods. The simulation of some test function optimization using BCC algorithm shows BCC algorithm a kind of potentially powerful optimization method worth of much more research.更多还原