Genetic Algorithm for QoS-Aware Web Service Selection Based on Chaotic Sequences

As a kind of service selection algorithm, genetic algorithm is a good way to select an optimal composite plan from many composite plans. Including crossover operation, mutation operation and selection operation, all the executions of GA rely on a randomly search procedure to seek the area of possible solutions. But, bad convergence and prematurity phenomenon of GA are produced by random sequences generation. They have become the obstacle for GA´s further application. To improve the convergence of genetic algorithm (GA) for Web service selection with global quality-of-service (QoS) constraints, chaos theory is introduced into the genetic algorithm with the relation matrix coding scheme. These chaotic laws are all based on the relation matrix coding scheme. During crossover and mutation process phases, chaotic time series are adopted instead of random ones. The effect of chaotic sequences and random ones is compared during several numerical tests. And, the performance of GA using chaotic time series and random ones is investigated. The simulation results on Web service selection with global QoS constraints have shown that the proposed strategy based on chaotic sequences can enhance GA´s convergence capability. The fitness is also improved after the chaotic approaches are introduced.