Using EDA-Based Local Search to Improve the Performance of NSGA-II for Multiobjective Semantic Web Service Composition

Service-oriented computing is a computing paradigm that creates reusable modules over the Internet, often known as Web services. Web service composition aims to accomplish more complex functions by loosely coupling web services. Researchers have been proposing evolutionary computation (EC) techniques for efficiently building up composite services with optimized non-functional quality (i.e., QoS). Some of these techniques employ multi-objective EC algorithms to handle conflict qualities in QoS for fully automated service composition. One recent state-of-art work hybridizes NSGA-II and MOEA/D, which allows the multi-objective service composition problem to be decomposed into many scalar optimization subproblems, where a simple form of local search can be easily applied. However, their local search is considered to be less effective and efficient because it is randomly applied to a predefined large number of subproblems without focusing on the most suitable candidate solutions. In this paper, we propose a memetic NSGA-II with probabilistic model-based local search based on Estimation of Distribution Algorithm (EDA). In particular, a clustering technique is employed to select suitable Pareto solutions for local search. Each selected solution and its belonged cluster members are used to learn a distribution model that samples new solutions for local improvements. Besides that, a more challenging service composition problem that optimizes both functional and non-functional quality is considered. Experiments have shown that our method can effectively and efficiently produce better Pareto optimal solutions compared to other state-of-art methods in the literature.