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A Multi-Strategy Combined Grey Wolf Optimization Algorithm

The Grey Wolf Optimization (GWO) Algorithm is a heuristic evolutionary algorithm inspired by the hunting and leadership mechanisms of the natural grey wolf. However, its location update equation has the disadvantages of strong development ability and weak exploration ability. To improve these shortcomings, we propose the RWCA-GWO algorithm. It occurs inspired by the random walk strategy and enhances the exploration ability of the leading wolves. Inspired by the analytic hierarchy process, a procedure for setting the weights of α, β, and δ are proposed. Besides, to promote the global convergence speed of the GWO algorithm, the initial population is generated by the chaotic initialization method. The simulation experiments are carried out with 11 test functions. The results show that for most cases, the performance of RWCA-GWO algorithm is significantly better than the standard grey wolf algorithm and other improved grey wolf algorithms under the same maximum fitness function evaluation times.