Improved whale optimization algorithm based on pinhole imaging and reverse learning strategy

A refined Whale Optimization Algorithm (MWOA), employing a hybrid strategy, is introduced to address challenges such as slow convergence, weak global search capability, low accuracy in problem-solving, and susceptibility to local optima. Leveraging the unique properties of the Chebyshev chaotic sequence, the algorithm strategically optimizes the initial population's placement. This ensures enhanced diversity among individuals, leading to a more rational distribution of locations, thereby boosting the overall optimization efficiency. Furthermore, the application of the reverse learning strategy from pinhole imaging enhances the leader's capacity to escape local optimal regions, subsequently elevating the algorithm's accuracy in problem-solving. During the whale spiral stage, an adaptive weight parameter is introduced to augment the algorithm's local search capabilities and refine convergence accuracy. The experimental results of 7 benchmark functions indicate that the MWOA algorithm outperforms any single-strategy improved algorithms mentioned above, thus confirming the effectiveness and superiority of the improvement method.