求解高维函数优化问题的混合蜂群算法

Hybrid Artificial Bee Colony Algorithm for Solving High-dimensional Function Optimization Problems

为了提高人工蜂群算法求解复杂优化函数的全局搜索能力,提出了多父体杂交算法、差分进化算法和蜂群算法的混合蜂群算法(Hybrid artificial bee colony algorithm,HABC)。HABC的核心在于,采用多父体杂交算子提高人工蜂群算法的全局搜索能力,通过淘汰相同个体保证群体的多样性,利用差分进化算子加快人工蜂群算法的收敛速度。高维函数优化问题的仿真结果表明,该算法全局搜索能力好,收敛速度快。

In order to enhance the global search ability of artificial bee algorithm in solving complex function optimiza- tion problem, a hybrid artificial bee colony algorithm （HABC） was proposed. HABC is based on multi-parent crossover and differential evolution, and the key points of it lie in： 1） employs multi-parent crossover to enhance the global search capability of the algorithm; 2） removes identical individuals from the population for maintaining the diversity; 3） adopts differential evolution operator to speed up the evolution. Experimental results on high-dimensional function optimization problems show that HABC possesses more powerful global search capability and better convergence rate.