Abstract
The Whale Optimization Algorithm (WOA) is a newly proposed metaheuristic optimization algorithm, which simulate humpback whales hunting behavior. Like other population-based algorithms, WOA generate its population randomly during the exploration and exploitation phases, which could
generate values far from the optimum solution or stuck the exploration around local optima. In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). The OWOA use the Opposition-based
method to enhance Whale Optimization Algorithm (WOA) performance. The OWOA looks for the solution in the opposite direction of suggested values to test if the opposite select has better solution. The OWOA is tested and compared with the original algorithm WOA and other metaheuristic methods.
The benchmark results prove the efficiency of the OWOA being more efficient than WOA