基于改进搜索策略的狼群算法

Wolf pack algorithm based on modified search strategy

针对狼群算法(WPA)存在的收敛速度慢、易陷入局部最优、人工狼交互性不理想等不足,提出一种基于改进搜索策略的狼群(MWPA)算法。对游走行为以及召唤行为引入交互策略,促使人工狼之间进行信息交流,提升狼群对全局信息的掌握,增强狼群的探索能力;对围攻行为提出自适应围攻策略,使算法具有调节作用,随着算法的不断进化,狼群围攻范围不断减小,算法开采能力不断增强,从而提高算法收敛速度。通过优化问题中6个典型复杂函数的仿真实验表明,与基于领导者策略的狼群搜索(LWCA)算法相比,改进搜索策略的狼群算法求解精度更高、收敛速度更快,更加适合函数优化问题的求解。

Aiming at the shortcomings of Wolf Pack Algorithm （WPA）, such as slow convergence, being easy to fall into local optimum and unsatisfactory artificial wolf interactivity, a wolf pack algorithm based on modified search strategy was proposed, which named Modified Wolf Pack Algorithm （ MWPA）. In order to promote the exchange of information between the artificial wolves, improve the wolves＇ grasp of the global information and enhance the exploring ability of wolves, the interactive strategy was introduced into scouting behaviors and summoning bebaviors. An adaptive beleaguering strategy was proposed for beleaguering behaviors, which made the algorithm have a regulatory role. With the constant evolution of algorithm, the beleaguered range of wolves decreased constantly and the exploitation ability of algorithm strengthened constantly. Thus the convergence rate of algorithm was enhanced. The simulation results of six typical complex functions of optimization problems show that compared to the Wolf Colony search Algorithm based on the strategy of the Leader （ LWCA）, the proposed method obtains higher solving accuracy, faster convergence speed and is especially suitable for function optimization problems.