WOSCA: A Hybrid Algorithm of Whale Optimization Algorithm and Sine Cosine Algorithm for Large-scale Optimization Problems

The whale optimization algorithm (WOA) and sine cosine algorithm (SCA) exhibit limitations, such as premature convergence and local optima, for solving large-scale optimization problems. To address this problem, a novel hybrid algorithm called WOSCA is proposed. WOSCA leverages orthogonal Latin squares to obtain the initial population with balanced dispersion and neat comparability, and integrates the search mechanism of SCA into the WOA to enhance and balance the algorithm's exploration and exploitation. Moreover, to avoid falling into the local optimum and enhance the diversity of the population, a dynamic inertia weight strategy is introduced for an exhaustive search of nearby space. Twenty high-dimensional benchmark functions are selected to evaluate the effectiveness of the proposed method. The results demonstrate that WOSCA has better convergence accuracy and stronger robustness when solving large-scale optimization problems.