Whale Optimization Algorithm Using Pinhole Imaging Reverse Learning and Golden Sine Strategy

Abstract

While handling problems of certain complex scene optimization, the Whale Optimization Algorithm (WOA) algorithm may be affected by precocious convergence or local optimal solutions, resulting in the accuracy of low convergence and stagnation of dimensional population. To address these limitations, this research presents a whale optimization algorithm, which is established on pinhole imaging reverse learning and the golden sine strategy (LWOAG). Firstly, LWOAG employs pinhole imaging reverse learning to determine the reverse solution for optimal individual in the population, thereby improving the population's quality and algorithm convergence ability. Secondly, LWOAG utilizes the golden sine operator to perform greedy selection after the whale completes the search update, thus extending the search range and increasing the algorithm's global search capacity. Finally, after conducting comprehensive tests on 12 benchmark functions, LWOAG outperforms other enhanced whale optimization algorithms and intelligent algorithms in terms of accuracy and stability.