Optimal Web Service Selection based on Multi-Objective Genetic Algorithm

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

“…Genetic-based techniques for selecting the optimal Web service composition have been proposed in [2,5,10,11,14,[20][21][22] (see Table 1). In the approaches presented in Table 1, a genetic individual is mapped on a composition solution encoded using discrete representations (e.g.…”

“…In these approaches, each ant builds a composition solution in each algorithm iteration by starting from the graph origin and by probabilistically choosing candidate services to be added to its partial solution. The probability of choosing a candidate service Roulette-based and elitism selection operator, two point crossover, random mutation [10] Solution: n-tuple QoS, penalty-based Roulette-based selection, uniform and hybrid crossover, random mutation [21] Solution: binary QoS-based Roulette-based selection, one point crossover, random mutation [22] Solution: binary QoS-based Elitism, two-point crossover, random mutation [14] Solution: integer array QoS, semantic, penalty, constraint-based Elitism, multipoint crossover, random mutation [20] Solution: hybrid encoding QoS-based Roulette-based selection, one point crossover, random mutation [2] Solution: hybrid encoding QoS, penalty, customer satisfaction-based Not specified [11] Solution: hybrid encoding QoS-based One point crossover, random mutation depends on the pheromone level associated to the edge in the abstract workflow connecting the current service to the candidate service and on heuristic information. In [23,31], the pheromone reflects the QoS attributes of the candidate service, while in [15] the pheromone is a numerical value equal for each edge connecting two services in the graph of services.…”

Many-Objective Evolutionary Algorithms in the Composition of Web Services

“…A work that addresses the problem by using genetic algorithms is presented by Vanrompay et al [17]. However, both mentioned works treat the multiple QoS indexes optimization problem as a single-objective problem, which inevitably miss some feasible solutions [18]. The optimization in the selection of Web services based on multi-objective evolutionary algorithms is presented by Wang and Hou [18].…”

“…However, both mentioned works treat the multiple QoS indexes optimization problem as a single-objective problem, which inevitably miss some feasible solutions [18]. The optimization in the selection of Web services based on multi-objective evolutionary algorithms is presented by Wang and Hou [18]. This work uses the concept of Pareto dominance to determine the probability of reproduction of each individual.…”

Ant-Inspired Technique for Automatic Web Service Composition and Selection

“…In [7] and [8], genetic algorithms are used to find the optimal composition solution. The composition method is based on a given service abstract workflow, where each abstract service has a set of concrete services with different QoS values associated.…”