A memetic NSGA-II with EDA-based local search for fully automated multiobjective web service composition

Chen Wang, Hui Ma, Gang Chen, Sven Hartmann

Abstract: Web service composition aims to provide added values by loosely coupling web services to accommodate users' complex requirements. Evolutionary computation techniques have been used to efficiently find near-optimal composite services to satisfy users' requirements reasonably well. Often, the quality of a composite service is measured by two important quality criteria that are related to the non-functional quality (i.e., Quality of service, QoS for short) and function quality (i.e., Quality of semantic matchmaking, QoSM for short). One recent work [2] proposed a Hybrid method that combines NSGA-II and MOEA/D with swap-based local search to enhance the performance of NSGA-II. This Hybrid method handles two quality criteria in QoS as two trade-off objectives. However, the local search of this method 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 EDA-based local search. Particular, EDA performs the local improvements of a few well-selected composite services in different regions of the Pareto front. We also aim to handle two practical trade-off objectives with respect to QoS and QoSM. Our experiments have shown that our proposed method outperforms the recent state-of-the-art algorithms and the baseline NSGA-II method with respect to effectiveness and efficiency.