协调探索和开发能力的改进灰狼优化算法

提出一种协调探索和开发能力的灰狼优化算法.利用佳点集方法初始化灰狼个体的位置,为全局搜索多样性奠定基础;为协调算法的全局探索和局部开发能力,给出一种基于正切三角函数描述的非线性动态变化控制参数;为加快算法的收敛速度,受粒子群优化算法个体记忆功能的启发,设计一种新的个体位置更新公式.10个标准函数的测试结果表明,改进灰狼优化(IGWO)算法能够有效地协调其对问题搜索空间的探索和开发能力.

Improved grey wolf optimization algorithm coordinating the ability of exploration and exploitation

An improved grey wolf optimization(IGWO) algorithm is proposed to solve global continuous optimization problems. The good point set method is used to initiate the grey wolves individuals'' position, which strengthens the diversity of initial individuals in the global searching process. A nonlinear strategy based on the tangent trigonometric function for updating the control parameter is given to balance the exploration and exploitation abilities of the proposed algorithm. Inspired by the particle swarm optimization(PSO) algorithm, a new position update equation of individuals by incorporating the information of individual historical best solution into the position update equation is designed to speed up convergence. The experimental results and comparisons with the classical GWO algorithm and other improved GWO algorithms using a set of well-known benchmark test functions show that the proposed IGWO algorithm can balance the exploration and exploitation to the problem''s solution space effectively during evolution.