Improved Grey Wolf Algorithm Based on Nonlinear Control Parameter Strategy

Aimed at the problem of the grey wolf optimizer (GWO) algorithm of low convergence speed and easily falling into local optimum, this paper proposes a nonlinear control parameter strategy based on sinusoidal function (GWO-SIN) and a nonlinear control parameter combination (GWO-COM) strategy. In order to verify the validity and advantages and disadvantages of the proposed nonlinear control parameter strategies, selected the adjustment parameter values of GWO-COM from five different adjustment parameter values by test results of simulation of four benchmark functions. Then, this paper compares against the original GWO and four GWO based on different nonlinear control parameter strategies on a set of well-known benchmark functions. Experimental results show that the nonlinear control parameter combination (GWO-COM) strategy is superior to the others and the IGWO (an improved grey wolf algorithm for global optimization), and the optimization result of the GWO-SIN algorithm is similar to GWO-COM in the multi-peak test function.