基于欧氏距离与多种搜索策略的人工蜂群算法

ABC algorithm based on Euclidean distance and multiple search strategies

针对人工蜂群算法收敛速度较慢、收敛精度低和探索能力强但开发能力弱等不足,提出了一种改进的人工蜂群算法(IABC)。利用欧氏距离获得局部最优蜜源,增强蜜源结构相似性的联系与优秀蜜源的影响,增强蜜源的开发能力并加快算法的收敛速度;搜索过程中,根据不同的搜索阶段,选择用不同的搜索策略来增加种群的多样性,以避免算法早熟收敛,平衡探索能力与开发能力。仿真实验结果表明:IABC算法可以有效提高收敛速度与解的精度。

Aiming at the disadvantages of artificial bee colony algorithm,such as slow convergence speed,low convergence precision,strong exploration ability but weak development ability,an improved artificial bee colony（ IABC） algorithm is proposed. In order to enhance the development ability of nectar source and accelerate the convergence speed of the algorithm,the algorithm uses the Euclidean distance to obtain the local optimal nectar source,enhance the connection of similarity of nectar source structure and the influence of the excellent nectar source; according to different search stage,choose different search strategies to increase the diversity of the population so as to avoid premature convergence of the algorithm in search process,which is conducive to balance the ability for exploring and developing. The simulation results show that the improved algorithm can effectively improve convergence speed and solution precision.