A Novel Grey Wolf Optimizer with Random Walk Strategies for Constrained Engineering Design
Tong Han, Xiaofei Wang, Yajun Liang, Zhenglei Wei, Yawei Cai · 2018
Grey wolf optimizer (GWO) is a widely applied nature-inspired optimization algorithm with simple search mechanism, which suffers the deficiency of premature convergence. To improve its exploration performance and avoid trapping in to stagnation, we integrate the basic GWO with two random walk strategies, Gaussian random walk and Lévy walk, and this new GWO extension is named RWGWO. The performance of RWGWO is compared with those of other comprising GWO variants by using several standard benchmarks. Moreover, RWGWO is applied in solving constrained engineering design problems. With a view to the experimental results, RWGWO has remarkable performance compared with the competitors in this work in terms of convergence speed and accuracy.