WGO: a similarly encoded whale-goshawk optimization algorithm for uncertain cloud manufacturing service composition

作者：Kezhou Chen, Tao Wang, Huimin Zhuo, Lianglun Cheng · 发表于：Autonomous Intelligent Systems · 年份：2025 · DOI：10.1007/s43684-025-00089-x · 被引用次数：5 · 研究领域：Advanced Manufacturing and Logistics Optimization、IoT and Edge/Fog Computing、Digital Transformation in Industry

Abstract Service Composition and Optimization Selection (SCOS) is crucial in Cloud Manufacturing (CMfg), but the uncertainties in service states and working environments pose challenges for existing QoS-based methods. Recently, digital twins have gained prominence in CMfg due to their predictive capabilities, enhancing the reliability of service composition. Heuristic algorithms are widely used in this field for their flexibility and compatibility with uncertain environments. This paper proposes the Whale-Goshawk Optimization Algorithm (WGO), which combines the Whale Optimization Algorithm (WOA) and Northern Goshawk Optimization Algorithm (NGO). A novel similar integer coding method, incorporating spatial feature information, addresses the limitations of traditional integer coding, while a whale-optimized prey generation strategy improves NGO’s global optimization efficiency. Additionally, a local search method based on similar integer coding enhances WGO’s local search ability. Experimental results demonstrate the effectiveness of the proposed approach.