基于改进邻域搜索策略的人工蜂群算法

针对人工蜂群算法存在易陷入局部最优、收敛速度慢的缺陷,提出一种改进邻域搜索策略的人工蜂群算法.首先,将混沌思想和反向学习方法引入初始种群,设计混沌反向解初始化策略,以增大种群多样性,增强跳出局部最优的能力;然后,在跟随蜂阶段根据更新前个体最优位置引入量子行为模拟人工蜂群获取最优解,通过交叉率设计更新前个体最优位置,并利用势阱模型的控制参数提高平衡探索与开发的能力,对观察蜂邻域搜索策略进行改进,以提高算法的收敛速度和精度;最后,将改进人工蜂群算法与粒子群算法、蚁群算法以及其他改进人工蜂群算法进行比较,利用12个标准测试函数进行仿真分析.结果表明,改进算法不仅提高了收敛速度和精度,而且在高维函数优化方面具有一定的优势.

Artificial bee colony algorithm based on improved neighborhood search strategy

As to overcome the drawback of easily falling into local optimum and slow convergence rate of the conventional artificial bee colony algorithm, this paper proposes an artificial bee colony algorithm based on the improved neighborhood search strategy. Firstly, in order to enhance the diversity of population and prevent local optimum, a kind of chaotic anti-base initialization mechanism is designed according to the chaotic thoughts and opposed-based learning method. Then, in the following stage of following bee stage, the quantum behavior is introduced to simulate the optimal solution of the artificial bee according to the optimal position of the former individual, the optimal position of the former individual is designed with crossover, and the control parameters of the well model is used to improve the balance exploration and development capability, a strategy of neighborhood search improvement strategy in observation is designed, to improve the convergence accuracy of the algorithm, Finally, the proposed algorithm is compared with the particle swarm optimization algorithm, ant colony algorithms, and other improved artificial colony algorithm, and simulation analysis is made on 12 standard test functions, the results show that the proposed algorithm not only improves the convergence speed and accuracy, but also has certain advantages in terms of high-dimensional function optimization.