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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.本文针对传统鲸鱼优化算法（Whale Optimization Algorithm，WOA）的缺陷展开分析，其主要问题包括易陷入局部最优、收敛速度缓慢，且针对高维复杂优化问题的全局搜索能力不足。为此，本文提出一种改进型鲸鱼优化算法（GWOA）以解决上述问题。该算法通过融合自适应参数调整、增强型猎物包围以及正余弦搜索等多种改进策略，显著提升了全局搜索能力与收敛效率。但GWOA也提升了计算复杂度，在处理大规模问题时可能导致更长的计算时长，且在高维场景下仍有可能陷入局部最优。为验证GWOA的有效性，本文开展了多组实验：首先选取23个经典基准测试函数，涵盖单峰、多峰以及组合型优化问题，并将GWOA与其他基础元启发式算法、优秀WOA变体以及最新提出的算法进行对比；随后针对GWOA开展了可扩展性对比实验。实验结果表明，在多数测试函数中，GWOA的收敛速度与求解精度均优于其他算法，尤其在多峰与组合型优化问题中表现突出，整体效率（Overall Efficiency，OE）值达74.46%。在压力容器设计、弹簧设计等工程优化问题中，GWOA可有效降低成本并满足约束条件，展现出更强的稳定性与优化能力。综上，GWOA通过多策略融合显著提升了全局搜索能力、收敛速度与求解稳定性，在求解复杂优化问题中展现出巨大潜力，可为工程优化应用提供高效工具。