## 1 Introduction  
As leading-edge technologies like cloud computing, big data, and the Internet of Things (IoT) evolve quickly, the traditional manufacturing industry is undergoing a profound transformation, gradually transitioning into the intelligent manufacturing era of Industry 4.0 [1]. One of the essential technologies aiding this transformation is cloud manufacturing (CMfg), which promotes the digitization, intelligence, and interconnectivity of the manufacturing process by utilizing cloud computing and related technologies to achieve a more efficient and flexible production system [2, 3]. A new connected manufacturing framework, called cloud manufacturing, has emerged, which utilizes virtualization technologies to transform physical manufacturing assets (e.g., machinery, data, and program) into virtual manufacturing cloud services [4]. These services are then integrated into a cloud platform which provides on-demand, flexible and efficient access to clients. This approach not only enables organizations to quickly adapt to market changes and improve competitiveness but also ensures a more balanced allocation of resources across the manufacturing industry, ultimately improving the efficiency and competitiveness of the entire manufacturing industry. A key aspect of cloud manufacturing involves solving complex manufacturing tasks submitted by clients. Generally, these tasks can be broken down into multiple subtasks, each requiring a different type of manufacturing resource. The challenge is to efficiently select and orchestrate these resources within the cloud platform to enable seamless collaboration between geographically dispersed manufacturing assets [5]. Achieving optimal resource utilization while maximizing user satisfaction is the fundamental goal of cloud manufacturing. However, this poses a significant research problem due to the complexity of matching different subtasks with appropriate resources under various constraints such as cost, time, reliability, and availability [6]. Researchers have investigated a variety of optimization strategies to meet this difficulty, including heuristic and metaheuristic algorithms, aimed at improving the efficiency and effectiveness of allocating resources and combining services in cloud manufacturing environments [7]. The issue of QoS-aware cloud service composition (QoS-CSC) [8] in cloud manufacturing has garnered significant interest. The QoS-CSC problem is NP-hard, making the problem difficult for conventional approaches to effectively identify the optimal solution, while the metaheuristic algorithms show a better performance for this type of problem [9]. [PERSON] et al. [10] presented a unique metaheuristic technique called artificial rabbit optimization (ARO), which was characterized by few parameters and simple implementation. Presently, numerous real-world optimization problems using the ARO method have been solved, such as the constrained engineering problem [11], the cogeneration economic dispatch problem [12], the optimal power flow problem [13], determining the PEM fuel cell parameters [14], structural damage identification [15], and optimization of the deep LSTM network for predicting stock price [16]. However, ARO also exhibits drawbacks like inadequate exploitation capability, lower population variety in later stages, and a tendency to become stuck in the local optima [17]. Addressing the limitations of ARO, presented in this study is a multi-strategy improved artificial rabbit optimization (MIARO) technique. Within MIARO, the algorithm's search capabilities globally and locally are improved through the use of Levy flight and golden sine methods, while an Archimedean spiral mutation is introduced to boost its diversity. These enhancements not only mitigate the intrinsic weaknesses of ARO but also render MIARO more adept at handling complex optimization tasks. Subsequently, the improved MIARO algorithm is tested on engineering optimization problems and QoS-CSC issues to showcase its efficacy in practical applications.