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

Volume 17, Issue 2, pp 288-292 http://dx.doi.org/10.22436/jmcs.017.02.10

Publication Date: June 15, 2017 Submission Date: December 10, 2016

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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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Keywords

• Particle swarm optimization
• evolutionary computation
• opposition-based learning
• global optimization.

MSC

•  68T05
•  68W25
•  90C26

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