关键词: 细菌觅食, 算法优化, 自适应学习, Tent映射, 高维函数优化, 局部搜索

Abstract:

An adaptive bacterial foraging optimization algorithm is presented due to the classic optimization algorithm's poor performance when optimizing high-dimensional complex functions. The fixed chemotactic step is improved as the adaptive sliding step which decreases nonlinearly with the result of strengthening the ability of local search. The adaptive dimension learning method for the optimal bacterium in the current cycle of chemotaxis is proposed so as to increase the accuracy of the solution and enhance the search efficiency. The elite bacterium is used as the initial point for Tent chaotic mapping to initialize the position of bacteria which meet the conditions of migration, and therefore the convergence speed of the algorithm is accelerated. Experimental result indicates that the algorithm outperforms the classic algorithm both in terms of  solution accuracy and convergence speed. And, the algorithm has a higher efficiency.

Key words: bacterial foraging, algorithm optimization, adaptive learning, tent map, high-dimensional function optimization, local search