## 1 Introduction
[PERSON] [9] introduced an adaptive robust SCOS model, which incorporates an alternative service invocation strategy. This model evaluates task delays based on whether to wait for service recovery or to invoke alternative services, thereby serving as a basis for robustness assessment. However, industrial software integration systems operate in environments that demand high real-time performance and reliability. In such contexts, traditional approaches--such as pre-configured alternative services or dynamic adjustment mechanisms--are often limited in practical applicability when dealing with service anomalies. Therefore, it is necessary to introduce evaluation metrics that more accurately reflect the system's capability for sustained service delivery. Availability, as a key metric for assessing a system's ability to continuously provide services [10], not only encompasses dimensions such as MTBF and failure recovery efficiency, but also integrates aspects of reliability, robustness, and other related quality attributes. Hence, availability should be considered as an independent performance evaluation criterion. The MOGWO has been widely applied to multi-objective optimization problems in engineering due to its advantages such as fast convergence speed, a small number of parameters, and ease of adjustment. [PERSON] et al. [11] integrated an improved MOGWO with the TOPSIS method to optimize process parameters in high-speed dry hobbing. [PERSON] et al. [12]employed MOGWO to achieve adaptive denoising of acoustic signals from gas pipeline leaks under multiple operating conditions. [PERSON] et al. [13] proposed a MOGWO algorithm combined with multiple search strategies for the multi-objective scheduling problem of cascade hydropower stations. [PERSON] et al. [14] combined NSGA-II with MOGWO, incorporating demand response and uncertainty modeling of renewable energy to achieve peak-valley load regulation in power systems. [PERSON] et al. [15] proposed an IMOGWO incorporating a predatory behavior-inspired search strategy to address the collaborative optimization scheduling problem in green manufacturing within distributed heterogeneous semiconductor wafer fabrication facilities. [PERSON] et al. [16] proposed a MOGWO method based on machine learning to realize the collaborative optimization of the safety of lightweight automobile seat frames. [PERSON] et al. [17] proposed a hunting search strategy based on dimension learning to address global optimization and engineering design problems, aiming to alleviate issues such as lack of diversity and premature convergence. Despite the extensive research efforts devoted to the improvement and application of the MOGWO method, its convergence performance remains suboptimal, and it is prone to becoming trapped in local optima. To address the resource composition optimization challenges in current industrial software integration, this study proposes a multi-objective optimization model that balances performance and reliability, guided by both QoS constraints and service availability requirements. To efficiently solve this model and improve solution quality, a HEMOGWO algorithm is introduced. The algorithm enhances the quality of the initial population using Tent chaotic mapping and improves global search capability and diversity by integrating a differential evolution strategy with Levy flight, effectively avoiding local optima. Experimental validation is conducted on both standard benchmark functions and real-world industrial software composition scenarios, demonstrating that the proposed method offers significant advantages over existing approaches in terms of solution distribution, convergence speed, and stability.