基于分段搜索策略的自适应差分进化人工蜂群算法

针对人工蜂群算法在求解函数优化问题时存在的探索能力强，而开发能力不足和收敛性能差的问题，本文提出一种基于分段搜索策略的自适应差分进化人工蜂群算法。该算法将改进后的差分进化算法中的变异操作引入到观察蜂的局部搜索策略中，让观察蜂在雇佣蜂逐维变异后的当前最优解周围进行局部搜索，并采用分段搜索的方式更新蜜源，以提高其局部搜索能力。仿真实验结果表明，与基本人工蜂群算法相比，改进后的算法有效地平衡了算法的探索能力和开发能力，并提高了算法的寻优精度和收敛速度。

Adaptive Differential Evolution Artificial Bee Colony Algorithm Based on Segmental-search Strategy

An Adaptive differential evolution Artificial Bee Colony (ABC) algorithm based on segmental-search strategy is proposed, in order to overcome the problems of good exploration but poor at exploitation and poor convergence of conventional algorithm when using ABC algorithm to solve function optimization. In this algorithm, the mutation in differential evolution algorithm is introduced into the local search process of onlooker bees, and then, onlooker bees could do the local search around the current optimal solution which after employed bees dimension variation, and segmental-search strategy is used to improve the updating rate of food sources, which aims to improve the local search capability of the algorithm. Simulation results of six classic functions show that compared with the basic ABC algorithm, the modified ABC algorithm effectively balances the exploration and exploitation, and greatly improves the accuracy of solution and convergence rate.