Running environment.

This paper analyzes the shortcomings of the traditional Whale Optimization Algorithm (WOA), mainly including the tendency to fall into local optima, slow convergence speed, and insufficient global search ability for high-dimensional and complex optimization problems. An improved Whale Optimization Algorithm (GWOA) is proposed to overcome these issues. By integrating several improvement strategies, such as adaptive parameter adjustment, enhanced prey encircling, and sine-cosine search strategies, GWOA significantly enhances global search ability and convergence efficiency. However, GWOA increases computational complexity, which may lead to longer computation times when handling large-scale problems. It may also fall into local optima in high-dimensional cases. Several experiments were conducted to verify the effectiveness of GWOA. First, 23 classic benchmark functions were tested, covering unimodal, multimodal, and compositional optimization problems. GWOA was compared with other basic metaheuristic algorithms, excellent WOA variants, and the latest algorithms. Then, a comparative scalability experiment is performed on GWOA. The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. In engineering optimization problems, such as pressure vessel design and spring design, GWOA effectively reduced costs and met constraints, demonstrating stronger stability and optimization ability. In conclusion, GWOA significantly improves the global search ability, convergence speed, and solution stability through multi-strategy integration. It shows great potential in solving complex optimization problems and provides an efficient tool for engineering optimization applications.