ORCA Optimization Algorithm: A New Meta-Heuristic Tool for Complex Optimization Problems

In this paper, a new meta-heuristic algorithm called Orca Optimization Algorithm (OOA) is proposed for complex and nonlinear optimization problems, especially practical engineering problems. This developed method is based on unique wave-washing orcas hunting technique. In the OOA, different random candidates (orcas) implement the cunning tactic of regularly hunting in packs and building waves to wash seals off drifting piece of ice. Application of the proposed algorithm on some benchmark functions including Schaffer, Griewank, Ackley, and Rosenbrock proved its excellent capability in comparison with other similar algorithms such as Genetic Algorithm (GA), Flower Pollination Algorithm (FPA), Harris Hawk Optimization (HHO) algorithm, and Imperialist Competitive Algorithm (ICA). Moreover, the performance of the proposed optimization algorithm applied on real engineering problems. In this experiment, OOA applied for online parameter estimation of photovoltaic modules equivalent circuit and optimal design of brushless direct current (BLDC) motor. The numerical analysis and obtained simulation results of the experiments showed the excellent performance of OOA.