Weighted distance Grey wolf optimizer for global optimization problems

Grey Wolf Optimizer (GWO) is one of the recently introduced Swarm Intelligence (SI) algorithms, developed by the inspiration from grey wolves prey search characteristics. The GWO algorithm imitate the hierarchical leadership and hunting mechanism of grey wolves in nature. The GWO simulates the major steps of grey wolves like hunting, searching for prey, encircling and attacking. The GWO move the wolves pack toward prey by updating location vector, which is an average of best locations of the pack. This paper addresses the GWO issues and proposes the Weighted distance Grey Wolf Optimizer (wdGWO). In proposed wdGWO algorithm, the location update strategy is modified and weighted sum of best locations is used instead of just a simple average. The proposed algorithm is well tested over set of complex benchmark problems and the performance is comprehensively compared with SI algorithms counterpart. The dimensions of the problems are varied from 10 to 50 for fair comparison among basic state of-the-art. Simulation results supports the superior performance of the proposed algorithm.