Abstract
For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.
Highlights
Service-oriented architecture (SOA) is an emerging style of software architecture that reuses and combines loosely coupled services for building, maintaining, and integrating applications in order to improve productivity and cost effectiveness throughout the application life cycle [1]
In order to speed up the process of obtaining the feasible particle position and optimization process, a local search strategy is proposed based on candidate service domination and incorporated into the algorithm hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO)
In algorithm HMDPSO+, global best solution is randomly selected from particle swarm Gbest, and it has been updated by comparison with mutated individual best particle position according to local search strategy
Summary
Service-oriented architecture (SOA) is an emerging style of software architecture that reuses and combines loosely coupled services for building, maintaining, and integrating applications in order to improve productivity and cost effectiveness throughout the application life cycle [1]. For the evaluation of the candidates in the environmental selection process, only the strength of a candidate being dominated is considered, but how many candidates it dominates is not considered These make the existing algorithms for this problem converge slowly, fall into local optimum, and are hard to get satisfying solution sets when problem scale is relatively large. The evaluation of these two algorithms including their parameters turning and comparative studies based on 4 different cases is given
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