Hybridization of Cuckoo Search and Firefly Algorithms for Selecting the Optimal Solution in Semantic Web Service Composition

Abstract

This chapter investigates how the Cuckoo Search and Firefly Algorithm can be hybridized for performance improvement in the context of selecting the optimal or near-optimal solution in semantic Web service composition. Cuckoo Search and Firefly Algorithm are hybridized with genetic, reinforcement learning and tabu principles to achieve a proper exploration and exploitation of the search process. The hybrid algorithms are applied on an enhanced planning graph which models the service composition search space for a given user request. The problem of finding the optimal solution encoded in the enhanced planning graph can be reduced to identifying a configuration of semantic Web services, out of a very large set of possible configurations, which maximizes a fitness function which considers semantics and

QoS-aware optimal and automated semantic web service composition with user’s constraints

“…We consider that a proper methodology for evaluating an optimization algorithm should consist of two steps: one step for establishing the optimal values of the adjustable parameters, and another step for evaluating the algorithm using the optimal configuration of the adjustable parameters. To establish the optimal values of the adjustable parameters the following two steps need to be addressed [49].…”

“…Hybrid approaches combine elements of AI planning or graph based and optimisation techniques for solving the composition problem with correct functionally, optimised QoS solutions [46,21,49].…”

“…the best solution) is discovered. Similarly, in [49] the authors propose two hybrid algorithms: Cuckoo Search and Firefly Algorithms, for selecting the optimal solution in semantic web service composition using the same model of enhanced planning graph. The proposed algorithms combine principles from population-based meta-heuristics with principles from trajectory-based meta-heuristics and reinforcement learning to optimize the search process in terms of execution time and fitness value reflecting the QoS and semantic quality of a solution.…”

“…Meta-heuristic algorithms are well known approximate algorithms which can solve optimization problems with satisfying results [65,39]. The works in [44,49,21,46] address the problem partially by exploiting AI planning or graph based automated service composition algorithms and selecting the optimal composition based on QoS. However, owning to not considering QoS constraints, the optimal solution in their approach may not satisfy user's needs, such as price and reputation constraints.…”