自适应搜索空间的混沌蜂群算法

针对人工蜂群（ABC）算法的不足，以种群收敛程度为依据，结合混沌优化的思想，提出一种改进的人工蜂群算法—自适应搜索空间的混沌蜂群算法（SA-CABC）。其基本思想是在原搜索区域的基础上，根据每次寻优的结果自适应地调整搜索空间，逐步缩小搜索区域，并利用混沌变量的内在随机性和遍历性跳出局部最优点，最终获得最优解。基于六个标准测试函数的仿真结果表明, 本算法能有效地加快收敛速度，提高最优解的精度, 其性能明显优于基本ABC算法，尤其适合高维的复杂函数的寻优。

Self-adapting search space chaos-artificial bee colony algorithm

To improve the performance of ABC algorithm, this paper proposed an improved ABC algorithm called self-adapting search space chaos artificial bee colony algorithm (SA-CABC). The main idea was to contract appropriately the ranges of search space according to the results of each optimization, and took use of the randomicity and ergodicity properties of the chaos to break away the local optima, and ultimately found the global optima. Experimental simulations show that the improved algorithm not only accelerates the convergence rate and improves its accuracy, but also effectively avoids the premature convergence problem. This improved algorithm is better than the basic ABC, and provides excellent performance in dealing high-dimensional complex problems.