A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem | Zendy

Hao Yin | Zendy; Changsheng Zhang | Zendy; Bin Zhang | Zendy; Ying Guo | Zendy; Tingting Liu | Zendy

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A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem

Author(s) -

Hao Yin,

Changsheng Zhang,

Bin Zhang,

Ying Guo,

Tingting Liu

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/252934

Subject(s) - particle swarm optimization , mathematical optimization , crossover , multi swarm optimization , local search (optimization) , algorithm , convergence (economics) , computer science , meta optimization , swarm behaviour , metaheuristic , set (abstract data type) , premature convergence , genetic algorithm , position (finance) , hybrid algorithm (constraint satisfaction) , local optimum , mutation , mathematics , constraint programming , artificial intelligence , constraint logic programming , finance , stochastic programming , economics , programming language , economic growth , biochemistry , chemistry , gene

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

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