融合混沌反学习与蜂群搜索算子的引力搜索算法

引力搜索算法是最近提出的一种较有竞争力的群智能优化技术，然而，标准引力算法存在的收敛速度慢、容易在进化过程中陷入停滞状态.针对上述问题，提出一种改进的引力搜索算法.该算法采用混沌反学习策略初始化种群，以便获得遍历整个解空间的初始种群，进而提高算法的收敛速度和解的精度.此外，该算法利用人工蜂群搜索策略很强的探索能力，对种群进行引导以帮助算法快速跳出局部最优点.通过对13个非线性基准函数进行仿真实验，验证了改进的引力搜索算法的有效性和优越性.

Improved Gravitational Search Algorithm with Chaotic Opposition-Based Learning and Artificial Bee Colony Search Operator

The gravitational search algorithm (GSA) is a relatively novel swarm intelligence optimization technique which has been shown to be competitive to other population-based intelligence optimization algorithms. However, there is still an insufficiency that is the low convergence speed of the standard gravitational search algorithm, and its being stalled easily in the evolutionary process. Considering those problems, an improved gravitational search algorithm is presented. A strategy of chaotic opposition-based learning is employed to generate an initial population, which makes it possible for the algorithm to achieve a better initial population, thus accelerating the convergence speed. In addition, the method makes full use of the exploration ability of the search strategy of artificial bee colony algorithm to guide the algorithm to jump out of the likely local optima. The results of numerical simulation experiment on a suite of 13 benchmark functions demonstrate the effectiveness and superiority of the improved gravitational search algorithm.