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Efficient Multi-objective Services Selection Algorithm Based on Particle Swarm Optimization

With the development of Web Service, it has become a key issue to select appropriate services from a large number of candidates for creating complex composite services according to users’ different QoS levels requirements. However, the existing service selection algorithms have many defects such as high time complexity, non-global optimal solutions, and poor quality solutions. To solve these defects, an efficient multi-objective services selection algorithm, EMOSS, is proposed in this paper based on particle swarm optimization. The essence of EMOSS is to model the service selection problem as a constrained multi-objective optimization problem. First the services in each sub-service set are sorted by their concept of domination, then the new sub-service set nSi, whose size is far less than the original one, is constructed and finally output pare to optimal set. The theoretical analysis and experimental results show that EMOSS can effectively obtain high quality solutions.