一种带规范知识引导的改进人工蜂群算法

针对数值函数优化问题，提出一种改进的人工蜂群算法.受文化算法双层进化空间的启发，利用信度空间中的规范知识引导搜索区域，自适应调整算法的搜索范围，提高算法的收敛速度和勘探能力.为保持种群多样性，设计一种种群分散策略，平衡群体的全局探索和局部开采能力，并且在各个进化阶段采用不同的方式探索新的位置.通过对多种标准测试函数进行实验并与多个近期提出的人工蜂群算法比较，结果表明该算法在收敛速度和求解质量上均取得较好的改进效果.

An Improved Artificial Bee Colony Algorithm with Guided Normative Knowledge

An improved artificial bee colony (ABC) algorithm is proposed to solve numerical function optimization problems. Inspired by the double evolutionary space of cultural algorithm,the proposed algorithm takes advantage of the normative knowledge of reliability space to guide the search region and control the radius of the local search space self-adaptively. Thus,the convergence speed and the exploitation ability are enhanced. In order to maintain diversity,a dispersal strategy is designed to balance global exploration and local exploitation of population capacity.Moreover,different approaches are used to explore new positions in various evolutionary stages. The experimental results demonstrate that the proposed algorithm outperforms existing artificial bee colony algorithms on a number of standard test functions both in convergence speed and solution quality.