A novel enhanced whale optimization algorithm for global optimization

One of the main issues with heuristics and meta-heuristics is the local optima stagnation phenomena. It is often called premature convergence, which refers to the assumption of a locally optimal solution as the best solution for an optimization problem and failure in finding the global optimum. Whale Optimization Algorithm (WOA) has demonstrated its merits in the optimization area. Though WOA is an effective algorithm, it may suffer from a low exploration of the search space. In this work, an enhanced WOA (WOAmM) is proposed. The mutualism phase from Symbiotic Organisms Search (SOS) is modified and integrated with WOA to alleviate premature convergence's inherent drawback. The addition of a modified mutualism phase with WOA makes the algorithm a balanced one to explore search space more extensively and avoid wasting computational resources in excessive exploitation. The proposed WOAmM method is tested on 36 benchmark functions and IEEE CEC 2019 function suite. It is compared with a wide range of algorithms, including improved WOAs and other meta-heuristics. Statistical analyses and convergence analysis are performed as well to examine its effectiveness and convergence speed. In addition, six real-world engineering optimization problems are solved by the proposed method. The performance is compared with a wide range of existing algorithms to inspect the problem-solving capability of WOAmM. The results demonstrate the performance improvement of WOAmM and its superiority over different algorithms.

Automated summary

An enhanced Whale Optimization Algorithm (WOAmM) is proposed in this work to overcome premature convergence issues by modifying the mutualism phase, leading to more comprehensive exploration of the search space. The method demonstrates improved performance and superiority over other algorithms in testing.