%0 Journal Article %T Particle swarm optimization with opposition-based learning and near neighbor interactions %A Wang, Jin %J Journal of Mathematics and Computer Science %D 2017 %V 17 %N 2 %@ ISSN 2008-949X %F Wang2017 %X 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. %9 journal article %R 10.22436/jmcs.017.02.10 %U http://dx.doi.org/10.22436/jmcs.017.02.10 %P 288-292