Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour - Hadi , Naghavipour (2022)
With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. This thesis core contribution is manifold. First, a mapping study was conducted to infer a framework for hybridization strategies by analyzing 71 papers selected out of the primary pool of 756 between 2008 and 2020. Moreover, it provided a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the primary outcome of this mapping study. As a result of this mapping study, five major hybridization strategies were identified in which two-third of solutions have been based on modifying algorithm operators or integration with another metaheuristic. An absolute majority of base algorithms for this problem were nature-inspired and population-based metaheuristics extended to complementary methods in hybrid solutions. Thus, slow convergence, local entrapment and stochastic behaviour were reported as their shortcoming. This thesis advocates incorporation of set theory as mathematical tools to transcend the boundary of metaheuristics. On that basis, the second contribution of this thesis is proposing a fast fuzzy evolutionary algorithm with minimal stochastic behaviour. Furthermore, this thesis contributes to the body of knowledge by introducing a novel method called Fuzzy Rough set Genetic Algorithm (FRGA) that take on efficiency of metaheuristics while reducing search space by leveraging the data mining aspect of rough set theory. Finally, this thesis revealed a parallel hybrid metaheuristic architecture and monitoring mechanism to provide immunity against premature convergence when the composition is performed in a subset of search space as the output of rough set-based heuristics. The experiment was conducted for 25 datasets generated incrementally from the real-world QWS dataset, where results were consistent and statistically significant. Inclusive of this writing, limitations, challenges and future direction are discussed in respect to this study finding, followed by conclusions.