基于平均熵的自适应人工蜂群算法

针对基本人工蜂群算法容易陷入局部最优和早熟等问题,提出一种改进的人工蜂群算法(ASABC)。利用平均熵机制初始化种群,增加种群的多样性,避免算法陷入早熟；同时,采用自适应调节邻域搜索步长的策略来提高算法的局部搜索能力,提升算法的计算精度；为了平衡算法的全局搜索能力和局部搜索能力,引入自适应比例选择策略来代替人工蜂群算法的适应度比例选择方法。对8个标准测试函数的仿真实验结果表明,与3种常见的智能优化方法相比,改进的算法具有显著的局部搜索能力和较快的收敛速度。

Self-adaptive Artificial Bee Colony Algorithm Based on Mean Entropy Strategy

In order to overcome the shortcomings that artificial bee colony (ABC) traps into local optima and premature easily,an improved artificial bee colony algorithm named ASABC algorithm was proposed.The new algorithm adopts mean entropy tactic to initialize population,which can increase the diversity of population and avoid the stagnation and premature.At the same time,the new algorithm adopts the strategy which can adjust the neighbour seletion step size adaptively to improve the local search ability and calculation precision.To balance the global search ability and the local search ability,the self-adaptive proportion selection strategy is used to replace the fitness proportion selection method of the ABC algorithm. The results of the simulation experiment on a suite of eight benchmark functions show that the new algorithm has remarkable local search ability and a faster convergence rate compared with three common intelligent optimization algorithms.