This paper proposes an improved ant colony optimization (ACO) based service composition algorithm for multi-cloud networks. The algorithm introduces a multi-pheromone mechanism to optimize quality of service (QoS) parameters such as latency and response time, and incorporates a mutation operation from genetic algorithms to avoid local optima and improve convergence speed. The service composition strategy utilizes service components distributed across multiple edge clouds to enhance service quality and meet diverse user requirements. The algorithm is evaluated through simulations, demonstrating superior performance in terms of QoS parameters and service stability compared to traditional ACO and other existing algorithms. The results show that the proposed algorithm achieves better QoS metrics while maintaining service stability, making it a promising solution for service composition in multi-cloud environments. The algorithm is designed to handle the dynamic nature of cloud services and the increasing complexity of service requirements in modern computing systems. The study highlights the importance of optimizing QoS parameters in service composition and presents a novel approach to achieve this through the integration of ACO with genetic algorithms. The proposed method is expected to improve the efficiency and effectiveness of service composition in cloud computing environments.This paper proposes an improved ant colony optimization (ACO) based service composition algorithm for multi-cloud networks. The algorithm introduces a multi-pheromone mechanism to optimize quality of service (QoS) parameters such as latency and response time, and incorporates a mutation operation from genetic algorithms to avoid local optima and improve convergence speed. The service composition strategy utilizes service components distributed across multiple edge clouds to enhance service quality and meet diverse user requirements. The algorithm is evaluated through simulations, demonstrating superior performance in terms of QoS parameters and service stability compared to traditional ACO and other existing algorithms. The results show that the proposed algorithm achieves better QoS metrics while maintaining service stability, making it a promising solution for service composition in multi-cloud environments. The algorithm is designed to handle the dynamic nature of cloud services and the increasing complexity of service requirements in modern computing systems. The study highlights the importance of optimizing QoS parameters in service composition and presents a novel approach to achieve this through the integration of ACO with genetic algorithms. The proposed method is expected to improve the efficiency and effectiveness of service composition in cloud computing environments.