一种自适应细菌觅食优化算法

针对在优化高维函数时,细菌觅食优化算法性能不佳的情况,提出了一种自适应细菌觅食优化算法.将固定的趋化步长改进为非线性递减的自适应游动步长,提高了算法的局部搜索能力;引入维度自适应学习算法,对每个趋化周期内得到的当前最优细菌进行维度自适应学习一次,提高了解的精度和搜索效率;将精英细菌作为Tent混沌映射的初始点对符合迁徙条件的细菌进行位置初始化,加快了算法的收敛速度.仿真结果表明,文中提出的算法在解的精度和收敛速度等方面均表现更优,具有更高的效率.

Adaptive bacterial foraging optimization algorithm

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.