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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Particle Swarm Optimization Combined with Query-Based Learning Using MapReduce
verfasst von : Jeng-Wei Lin, Wen-Chun Chi, Ray-I Chang
Erschienen in: Future Information Technology
Verlag: Springer Berlin Heidelberg
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Particle swarm optimization (PSO) has shown its effectiveness to solve many complex optimization problems. However, PSO sometimes may fall into a local optimal solution rather than the global optimal solution of a given problem. For large-scale optimization problems, several parallelized PSOs have been proposed in the literature. In this paper, a query-based learning (QBL) approach is adopted to help PSO jump out of a local optimum. An Oracle is introduced that answers PSO whether there are too many particles in a flat region of the solution space. If yes, the algorithm will redistribute some of the particles in the flat region to somewhere else. A parallelized implementation, referred to as MRPSO-QBL, is developed in the Apache Hadoop MapReduce framework, as the framework provides a simpler and better parallel programming paradigm. The experiment results on several benchmark functions have demonstrated that MRPSO-QBL can find better solutions and converge faster.