Improved Whale Optimization Algorithm Based on Nonlinear Adaptive Weight and Golden Sine Operator

Abstract

Whale optimization algorithm (WOA) is a swarm intelligence-based algorithm that simulates whale population predation in the sea. Aiming at the shortcomings of WOA such as low precision and slow convergence speed, an improved whale optimization algorithm based on nonlinear adaptive weight and golden sine operator (NGS-WOA) was proposed. NGS-WOA first introduced a non-linear adaptive weigh so that search agents can adaptively explore the search space, and balance the development and exploration stages. Secondly, the improved golden sine operator is incorporated into the WOA. Due to the special relationship between the sine function and the unit circle, traversing the sine function is equivalent to scanning the unit circle. The search agent performs an efficient search with a sine route so as to improve the convergence speed and global exploration capability of the algorithm. At the same time, the addition of the golden section coefficient allows search agents to exploit with a fixed shrink step. The search agent can develop to areas with excellent results, which improves the optimization accuracy and local exploitation ability of the algorithm. In the simulation experiments, the gold sine algorithm (GoldSA), whale optimization algorithm (WOA), particle swarm optimization (PSO) algorithm, firefly algorithm (FA), fireworks algorithm (FWA), sine cosine algorithm (SCA) and NGS-WOA were selected for comparison experiments. Then, the effectiveness of the proposed improved strategies is verified. Finally, the improved WOA is applied to high-dimensional optimization and engineering optimization problems. The experimental results show that the improved strategy can effectively improve the performance of the algorithm, so that NGS-WOA has the advantages of high global convergence and avoiding falling into local optimal values.