混沌搜索策略的改进人工蜂群算法

针对人工蜂群算法的蜂群缺乏多样性、全局和局部搜索能力差及收敛速度较慢,提出一种基于混沌搜索策略的改进人工蜂群算法。该算法通过载波映射,由混沌-决策变量的变换,产生新的邻域点,为采蜜蜂和被招募的观察蜂提供了更广阔的搜索空间和更优质的位置蜜源,增强蜂群多样性;同时,引进侦查蜂局部蜜源搜索较好地解决了算法易陷入局部极小的问题,改善了人工蜂群算法的收敛性能。最后由6个标准测试函数的仿真验证,得到基于混沌搜索策略的人工蜂群算法性能明显优于标准人工蜂群算法。

Improved artificial bee colony algorithm based on chaos searching strategy

The current artificial bee colony algorithm results in the swarm lacking diversity, and the global and local search abilities and convergence speed are slow. We propose an improved artificial bee colony algorithm based on a chaotic search strategy. We map the algorithm with the carrier using a chaos decision variable transformation, generating new neighborhood points, and recruiting bees within a broader search space and from better source locations, while enhancing swarm diversity. In addition, the investigation of a local honey bee search better solved the algorithm problem of the local minimum and improved the convergence property of the artificial bee colony algorithm. The most recent six simulation validations of the standard test functions using the proposed artificial bee colony algorithm, based on the chaotic search strategy, are significantly better than the performance results of the current artificial bee colony algorithm.