Research On Grey Wolf Optimization Algorithm And Its Application Based On Multi-Strategy

Grey Wolf Optimizer(GWO)is a meta-heuristic optimization algorithm that simulates the hunting process and social rank behavior of the North American Grey Wolf.GWO is intuitive and easy to implement,with few parameters and fast convergence speed.GWO algorithm and many improved GWOs have been widely used in many fields.However,with the increase of the problem scale and computational complexity,the existing GWOs can no longer meet the needs of optimization problems.Therefore,the design of GWO with better performance has important theoretical significance and application value.The main work of this paper is as follows:(1)In order to improve the insufficient exploration capability of GWO,congestion control strategy and global optimal search strategy are introduced into grey Wolf optimization algorithm.An Improved Grey Wolf Optimization(IGWO)algorithm is proposed,and the proposed algorithm is applied to the multiple UAVs task allocation The test results show that IGWO has obvious advantages in solving multi-UAVs task allocation problems,and is suitable for complex large-scale optimization problems.(2)In order to improve the optimization ability of the GWO,the last-place elimination strategy,nonlinear parameter coefficient and random disturbance strategy are introduced into the GWO.A Multi-Strategy Grey Wolf Optimization(MSGWO)algorithm is proposed and applied to PID parameter optimization.Test results show that MSGWO performs well in PID parameter optimization.(3)In order to improve the balance between exploration and development ability of GWO,a Feedback Grey Wolf Optimization algorithm(FGWO)based on feedback mechanism is proposed.The algorithm adjusts the exploration and development performance of the algorithm through the feedback of each optimization result.Make the exploration and development performance of the algorithm more balanced.The experimental results show that FGWO has a good balance between exploration and development ability in the optimization of microgrid dispatching,and has achieved good results.