Directional mutation and crossover for immature performance of whale algorithm with application to engineering optimization

Abstract In recent years, a range of novel and pseudonovel optimization algorithms has been proposed for solving engineering problems. Swarm intelligence (SIAs) have become popular methods, the whale algorithm (WOA) is one highly discussed SIAs. However, regardless novelty concerns about this method, basic WOA weak method compared to top differential evolutions particle swarm variants, it suffers from problem poor initial population quality slow convergence speed. Accordingly, in paper, increase diversity versions enhance performance WOA, new variant, named LXMWOA, proposed, based on Lévy initialization strategy, directional crossover mechanism, mutation mechanism. Specifically, introduction strategy allows populations be dynamically distributed search space enhances global capability WOA. Meanwhile, mechanism can improve local exploitation To evaluate its performance, using series functions three models problems, LXMWOA was with broad array competitive optimizers. The experimental results demonstrate that significantly superior exploration peers. Therefore, great potential used