Multi-objective IoT Service Composition with Replication Using Memetic NSGA-II with Bottleneck-Driven Local Search

The increasing complexity and variety of IoT systems require the integration of multiple services to meet a wide range of user needs. This paper addresses the challenge of multi-objective IoT service composition with replication problem by considering multiple Quality of Service (QoS) metrics such as response time and the number of selected service instances. We propose a new Memetic NSGA-II algorithm with Bottleneck-driven Local Search (MNSGA2-BLS) to effectively solve this difficult problem. By integrating genetic operations, clustering-based refinement, and a bottleneck-driven Estimation of Distribution Algorithm for local search, MNSGA2-BLS identifies and optimizes critical service instances causing QoS bottlenecks. This method leverages Pareto-optimal solutions to guide the local search refinement process, enhancing convergence and solution quality. Experimental results across various benchmark cases demonstrate that MNSGA2-BLS can outperform NSGA-II and several state-of-the-art algorithms, achieving superior results in both the hyper-volume and inverse generational distance metrics. This highlights the potential of MNSGA2-BLS to provide efficient and effective composite IoT services while addressing trade-offs between competing QoS objectives.