Multiple Strategies Boosted Orca Predation Algorithm for Engineering Optimization Problems

Abstract This paper proposes an enhanced orca predation algorithm (OPA) called the Lévy flight (LFOPA). LFOPA improves OPA by integrating (LF) strategy into chasing phase of and employing greedy selection (GS) at end each optimization iteration. enhancement is made to avoid entrapment local optima improve quality acquired solutions. a novel, efficient population-based optimizer that surpasses other reliable optimizers. However, owing low diversity orcas, prone stalling in some scenarios. In this paper, proposed for addressing global real-world challenges. To investigate validity LFOPA, it compared with seven robust optimizers, including improved multi-operator differential evolution (IMODE), covariance matrix adaptation (CMA-ES), gravitational search (GSA), grey wolf (GWO), moth-flame (MFO), Harris hawks (HHO), original on 10 unconstrained test functions linked 2020 IEEE Congress Evolutionary Computation (CEC’20). Furthermore, four different design engineering issues, welded beam, tension/compression spring, pressure vessel, speed reducer, are solved using its applicability. It was also employed address node localization challenges wireless sensor networks (WSNs) as example applications. Results tests significance show performs much better than competitors. simulation results superior competitors terms minimizing squared errors errors.