针对灰狼优化算法(Grey Wolf Optimizer,GWO)寻优精度低、收敛速度慢的问题,提出了一种基于IMQ惯性权重策略的自适应灰狼优化算法(ISGWO).该算法利用IMQ函数的特性,实现对惯性权重的非线性调整,从而更好地平衡算法的全局勘探能力和局部开发能力;同时,基于Sigmoid指数函数自适应更新个体位置,更好地搜索和优化问题的解空间.采用6个基本函数和29个CEC2017函数对ISGWO进行测试,并与6种常用的算法进行比较,实验结果表明ISGWO具有更优的收敛精度和速度.
Adaptive Grey Wolf Optimizer Based on IMQ Inertia Weight Strategy
Aiming at the problems of low optimization accuracy and slow convergence speed of grey wolf optimizer(GWO),this paper proposes an adaptive grey wolf optimization algorithm(ISGWO)based on IMQ inertia weighting strategy.This algorithm utilizes the properties of the IMQ function to achieve a nonlinear adjustment of the inertia weights,which better balances the global exploration ability and local exploitation ability of the algorithm.At the same time,it adaptively updates the position of in-dividuals based on the Sigmoid exponential function to better search and optimize the solution space of the problem.Six basic functions and 29 CEC2017 functions are used to test ISGWO and compare it with six commonly used algorithms,and the experi-mental results show that ISGWO has superior convergence accuracy and speed.
IMQ functionInertia weightAdaptiveGrey wolf optimizerConvergence speedOptimization accuracy
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