Research On Improvement And Application Of Grey Wolf Optimization Algorithm

Grey Wolf Optimization Algorithm(GWO)is a new swarm intelligence optimization algorithm proposed in 2014 by Australian scholar Mirjalili et al,which was mainly inspired by the strict social hierarchy mechanism and hunting behavior of grey wolf populations in nature.The GWO algorithm has the advantages of few adjustment parameters,simple principle,easy implementation,strong global search ability,and adjustable adaptive convergence factor.However,for the iteration mechanism of grey wolf optimization algorithm itself,it has some shortcomings such as low precision,slow convergence speed and easy to fall into local optimum in solving some optimization problems.Therefore,the basic grey wolf optimization algorithm still has a lot of research space.For the unconstrained optimization problems,in view of the shortcomings of GWO algorithm,this paper will introduce different strategies to improve the basic grey wolf optimization algorithm,and propose two different improved grey wolf optimization algorithms.Firstly,the convergence factor that decreases linearly with the number of iterations in the grey wolf optimization algorithm is replaced with a nonlinear decreasing convergence factor,and the spiral bubble net hunting behavior and nonlinear inertia weight are introduced to improve the position update formulas of the grey wolf optimization algorithm.The improved grey wolf optimization algorithm based on the hunting behavior of spiral bubble net is proposed.Compared with the GWO algorithm,the global search and local search capabilities of the obtained algorithm are more balanced,the algorithm has a faster convergence speed and higher solution accuracy.Then,inspired by the whale optimization algorithm,the spiral bubble net hunting behavior with Levy flight is introduced.The position update formulas of the surrounded hunting behavior of the grey wolf optimization algorithm are improved.And the random probability factor is used to randomly select different hunting behaviors.Greedy selection strategy is used to select the better grey wolf individuals before and after the improvement.Therefore,a hybrid grey wolf optimization algorithm based on the whale algorithm is proposed.Compared with the GWO algorithm,the obtained algorithm can effectively avoid falling into local optimum,with higher solution accuracy and faster convergence speed.The two proposed improved grey wolf optimization algorithms are numerically tested using standard unconstrained optimization test problems.The experimental data show that the two improved grey wolf optimization algorithms have best stability,fastest convergence speed and highest solution accuracy than GWO algorithm and other optimization algorithms.For the constrained optimization problems,the augmented Lagrange multiplier method is used to transform the constrained optimization problem into a series of bounded constrained optimization problems,and then the hybrid grey wolf optimization algorithm based on the whale algorithm is used to solve the obtained bounded constrained optimization problems.Therefore,a hybrid grey wolf optimization algorithm based on the augmented Lagrange multiplier method is proposed.Numerical experiments are carried out using the constrained optimization problem and compared with other optimization algorithms,the feasibility and effectiveness of the hybrid grey wolf optimization algorithm based on the augmented Lagrange multiplier method to solve the constrained optimization problem are verified.