A hybrid whale optimization algorithm for global optimization

Notwithstanding the superior performance of the Whale optimization algorithm (WOA) on a wide range of optimization issues, the exploitation in WOA gets more preference during the search process, thereby compromising the solution accuracy and diversity and also increases the chance of premature convergence. In this study, a novel modified WOA (m-SDWOA) is presented where the conventional WOA is combined with the modified mutualism phase of symbiotic organisms search (SOS), DE/rand/1/bin mutation strategy of differential evolution (DE), and commensalism phase of SOS. A new selection parameter gamma is introduced to select between exploration and exploitation phases of the algorithm. This overall arrangement balances the ability of the algorithm to explore or exploit. The algorithm's efficiency is verified through 42 benchmark functions and IEEE CEC 19 test suite and comparing the results with various state-of-the-art algorithms comprising basic methods, WOA variants, and DE variants. Statistical analyses like Friedman's test, box plot comparison, and Nemenyi multiple comparison tests are employed to check the proposed algorithm's consistency and statistical superiority. Finally, four real-life engineering design problems have been solved to confirm the problem-solving capability of the proposed m-SDWOA. All these analyses demonstrate the superiority of the proposed algorithm over the compared algorithms.