一种多目标人工蜂群算法

Multi-objective Artificial Bee Colony Algorithm

为将标准人工蜂群算法有效应用到多目标优化问题中,设计了一种多目标人工蜂群算法。其进化策略在利用精英解引导搜索的同时结合正弦函数搜索操作来平衡算法对解空间的开发与开采行为。另外,算法借助了外部集合来记录与维护种群进化过程中产生的Pareto最优解。理论分析表明:针对多目标优化问题,本算法能收敛到理论最优解集合。对典型多目标测试问题的仿真实验结果表明:本算法能有效逼近理论最优,具有较好的收敛性和均匀性,并且与同类型算法相比,本算法具有良好的求解性能。

This paper designed a multi-objective artificial bee colony algorithm in order to make it effectively apply to multi-objective optimization problem. The evolutionary strategy uses elite solutions to guide search, at the same time combines sine function searching operation to balance exploration and exploitation of solution space. In addition, the al- gorithm records and maintains the Pareto optimal solutions of evolutionary process with the aid of the external archive. The theoretical analysis shows that the proposed algorithm can converge to the theory optimal solution archive of multi- objective problem. In addition, simulations result indicate that the proposed algorithm can effectively close to theory op- timal solution archive, has good convergence and uniformity in solving typical multi-objective optimization problem, and compared with the same type of algorithms in references, it has good performance.