DPACO: Dynamic Tuning of ACO with Adaptive Strategy for QoS-Aware Web Service Composition - EL ALLALI, Naoufal; zannou, abderrahim; Mahmoudi, Omayma; Asaidi , Hakima; Bellouki , Mohamed (Statistics, Optimization & Information Computing, 2026)
With the proliferation of services available on the Web, modern systems increasingly require mechanisms able to orchestrate multiple services in order to meet complex business requirements. Web service composition is an efficient approach to transform these basic services into coherent composite solutions, while ensuring the scalability and flexibility essential in distributed environments. However, Web service composition (QoS-aware WSC) based on Quality of Service presents a major problem. For each task in a workflow, several candidate services are available, characterized by significant heterogeneity and variability. In order to select the optimal combination of services able to satisfy QoS constraints transforms the problem into a complex multi-objective optimization challenge that is difficult to solve in polynomial time, especially in large-scale and dynamic environment. In this paper, we proposed a new approach called Dynamic Parameter Ant Colony Optimization (DPACO), an enhanced variant of the ACO algorithm for QoS-aware Web service composition. DPACO introduces adaptive parameter control and a dynamic pheromone injection strategy, which improve the balance between exploration and exploitation. This adaptivity helps to avoid stagnation and local optimal, while reducing search time and ensuring robustness in dynamic and heterogeneous environments. Experimental evaluations conducted on multiple datasets demonstrate the superiority of the proposed approach compared to classical ACO and its variants (SACO, MOACO, FACO, and EFACO). Specifically, it achieves improvements of up to +15.2% over ACO, +12.6% over SACO, +9.8% over MOACO, +7.3% over FACO, and +11.6% over EFACO in terms of solution quality. Moreover, the adaptive injection and parameter adjustment mechanisms significantly reduce the computational time required to reach optimal composite services.