Grey Wolf Optimizer With a Novel Weighted Distance for Global Optimization

In this paper, a new grey wolf optimizer (GWO) variant based on a novel weighted distance (WD) called the GWO-WD algorithm is presented to solve global optimization problems. First, a modified position-updating equation formulated using the proposed strategy is employed to obtain additional information and improved global solutions. Then, several of the worst individuals are eliminated and repositioned using an elimination and repositioning strategy to improve the capability of the algorithm and avoid falling into local optima. The performance of the algorithm is verified by utilizing 23 widely used benchmark test functions, the IEEE CEC2014 test suite and three well-known engineering design problems. The simulation results of the proposed algorithm are compared with those of the standard GWO algorithm, three GWO variants and several existing methods, and the proposed algorithm is revealed to be very competitive and, in many cases, superior.