A parallel grey wolf optimizer combined with opposition based learning

Optimization methods based on swarm intelligence, have been used widely in science. These methods are mainly inspired from swarm behavior of animals in nature. Grey Wolf Optimizer (GWO) is a meta-heuristic approach simulating wolves' behavior while they are hunting. In this research, it has been tried to improve the final results of the original version of algorithm, compared with other common optimization approaches, using the techniques of opposition-based learning and parallelism. The obtained results from implementation and performing the improved algorithm on well-known benchmark functions indicate enhancement the convergence speed and precision in final results.