人工蜂群算法自2005年被Karaboga等人提出以来，以其操作简单、参数少、易于编程实现、收敛速度快等特点而受到越来越多的关注。2007年，Karaboga【2007】使用人工蜂群算法对多变量函数进行优化，并对由人工蜂群算法（ABC），遗传算法（GA），粒子温度算法（PSO）和粒子温度灵敏演化算法（PS-EA）产生的结果进行了比较。 结果表明，人工蜂群算法优于其他算法。2009年，Karaboga【2009】使用人工蜂群算法优化大量的数值函数，并对由人工蜂群算法（ABC），遗传算法（GA），粒子温度算法（PSO），差分演化算法（DS）和进化策略（ES）产生的结果进行了比较。 结果表明，人工蜂群算法因其控制参数少、操作简群算单优于或类似于其他算法。(Artificial Bee Colony (ABC) algorithm (Dervis Karaboga 2005 [1]; Karaboga and Basturk 2009[2]), simulating the intelligent social behavior of a group of bees, aims to solve the numerical optimization problem in a given condition. Many scientific theories and engineering applications in real life can be attributed to the numerical optimization problem. For applications where there is no optimal solution or approximate solution, ABC optimization algorithm can show its advantages in a short period of time and give it a term that can be terminated at any time. Initializing a set of random solution at the very beginning and searching the optimization value with iteration according to candidate solutions generated by a certain strategy, ABC algorithm solves the problems effectively and efficiently. Due to these advantages, ABC optimization algorithm has been increasingly popular since it has been proposed by Dervis Karaboga in 2005 [1].)