Service Pattern Fusion: Toward Self-Evolving of Service Ecosystems

A service ecosystem refers to a multilateral network composed of heterogeneous service entities, where the exchange of data, resources, and value through interactions among specific participants forms a service pattern. As service ecosystems like virtual hospital alliance (VHA) evolve towards large-scale, multi-domain integration to meet complex user needs, service pattern fusion has emerged as a fundamental approach to leverage data, resources, and value aggregation. By converging elements from multiple patterns, service pattern fusion enables the fulfillment of composite business objectives with reduced redundancy and lower costs. Existing works primarily address fusion requirements by reorganizing existing services through approaches such as service composition and business process management where only service functions and workflows are considered. However, they lack formalization of pattern fusion constraints and fail to support comprehensive integration of participants, data, resources, and value, let alone identifying optimal fusion solutions that account for participant collaboration and service integration. In this study, we formally define the Service Pattern Fusion Problem (SPFP) as an optimization task aimed at identifying the most efficient and cost-effective pattern by integrating, combining, and pruning elements from multiple patterns while preserving their objectives and meeting business constraints. We adapt traditional heuristic methods to SPFP and propose the Fusion-Oriented Confidence-Aware genetic algorithm (FoCa). FoCa dynamically adjusts the search space and transition probabilities in each iteration, achieving optimal fusion results with a 41.09% reduction in pattern loss and the fastest convergence. In addition, we designed a set of pattern features and conducted random fusion experiments on the public service pattern dataset S-SPD, to explore the correlation between those features and the optimization magnitude across various metrics. The analysis helps identify which types of service patterns benefit most from fusion, providing valuable insights for researchers and practitioners in both academic and engineering contexts.