Particle swarm optimization with opposition-based learning and near neighbor interactions


Authors

Jin Wang - School of Information, Linyi University, Linyi 276000, China.


Abstract

Particle swarm optimization (PSO) is recently proposed as population-based stochastic algorithm, which has shown excellent abilities in many optimization problems. In this paper, a hybrid PSO variant is presented to enhance its performance. The new algorithm is called OFDR-PSO which employs opposition-based learning (OBL) and fitness-distance-ratio (FDR). In order to verify the performance of OFDR-PSO, we test in on a set of well-known benchmark problems. Simulation results demonstrate that our proposed approach is effective and outperforms other four compared algorithms.


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ISRP Style

Jin Wang, Particle swarm optimization with opposition-based learning and near neighbor interactions, Journal of Mathematics and Computer Science, 17 (2017), no. 2, 288-292

AMA Style

Wang Jin, Particle swarm optimization with opposition-based learning and near neighbor interactions. J Math Comput SCI-JM. (2017); 17(2):288-292

Chicago/Turabian Style

Wang, Jin. "Particle swarm optimization with opposition-based learning and near neighbor interactions." Journal of Mathematics and Computer Science, 17, no. 2 (2017): 288-292


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