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

自主智能系统(英文) Pub Date : 2025-03-05 DOI:10.1007/s43684-025-00089-x

Kezhou Chen, Tao Wang, Huimin Zhuo, Lianglun Cheng

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引用次数: 0

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.

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WGO：一种类似编码的不确定云制造服务组成的鲸-苍鹰优化算法

服务组合与优化选择（SCOS）在云制造（CMfg）中至关重要，但服务状态和工作环境的不确定性对现有基于qos的方法提出了挑战。最近，数字孪生由于其预测能力，增强了服务组合的可靠性，在CMfg中获得了突出的地位。启发式算法以其灵活性和对不确定环境的兼容性被广泛应用于该领域。本文提出了鲸-苍鹰优化算法（WGO），该算法将鲸优化算法（WOA）和北苍鹰优化算法（NGO）相结合。一种包含空间特征信息的类似整数编码方法解决了传统整数编码的局限性，而鲸鱼优化的猎物生成策略提高了非政府组织的全局优化效率。此外，基于相似整数编码的局部搜索方法增强了WGO的局部搜索能力。实验结果证明了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

自主智能系统(英文)

CiteScore

3.90

自引率

0.00%

发文量