具有人工蜂群搜索策略的差分进化算法

针对差分进化算法易出现早熟现象和收敛速度慢等问题,提出一种具有人工蜂群搜索策略的差分进化算法.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.此外,为了提高算法的全局收敛速度,采用一种基于反学习的初始化方法.通过对12个标准测试函数进行仿真实验并与其他算法相比较,表明了所提出的算法具有较快的收敛速度和很强的跳出局部最优的能力.

Differential evolution with the search strategy of artificial bee colony algorithm

For the problems of premature convergence frequently appeared in differential evolution(DE) and its poor
convergence, a differential evolution with the search strategy of artificial bee colony algorithm is proposed. 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. In addition, to enhance the global convergent speed, when producing the initial population, the
opposition-based learning method is employed. Moreover, the performance of the proposed approach is testified on a suite of
12 benchmark functions and the comparisons with other algorithms are provided. Simulation results show that the proposed
approach has the better convergence rate and the strong ability of preventing premature convergence.