Abstract
Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm inspired by whale hunting behavior. Aiming at the defect that the spiral update mechanism in WOA may exceed the search range, three different spiral searching strategies are first proposed. The agents search with a more reasonable and broader route distribution so as to improve population diversity and traversal. Secondly, an improved sine cosine operator based on the convergence factor was proposed to improve the search efficiency of WOA, where sine search is used for global exploration and cosine search is used for local exploitation. The proposed convergence factor enables search agents to adaptively balance the exploration and exploitation phases with iterations. In the simulation experiment, the effectiveness of three spiral search strategies and sine cosine operator is verified. Then, the whale optimization algorithm (WOA), salp swarm algorithm (SSA), firefly algorithm (FA), moth-flame optimization (MFO) algorithm, fireworks algorithm (FWA), sine cosine algorithm (SCA) and improved WOA are selected for comparison experiments. Finally, the improved WOA is applied to two engineering problems (three-bar truss design problem and the welded beam optimization problem). The experimental results show that compared with other optimization algorithms, the improved WOA has the advantages of high search accuracy, fast convergence speed, and avoiding falling into local optimal values.