Multiple Strategies Boosted Orca Predation Algorithm for Engineering Optimization Problems

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