Particle swarm optimization with opposition-based learning and near neighbor interactions
- School of Information, Linyi University, Linyi 276000, China.
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.
- Particle swarm optimization
- evolutionary computation
- opposition-based learning
- global optimization.
R. Eberhart, Y. Shi, Comparison between genetic algorithms and particle swarm optimization, The 7th Annual Conference on Evolutionary Programming, San Diego (1998)
J. Kennedy, R. C. Eberhart, Particle swarm optimization, IEEE Int. Conference Neural Networks, Perth, Australia. , (1995)
J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Comput., 10 (2006), 281–295.
A. R. Malisia, H. Tizhoosh, Applying opposition-based ideas to the ant colony system, Proc. IEEE Swarm Intelligence Symposium, (2007), 79–87.
S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama, Opposition-based differential evolution, IEEE Trans. Evol. Comput., 12 (2008), 64–79.
J. Riget, J. S. Vesterstom, A diversity-guided particle swarm optimizer-the ALPSO, Dept. Comput. Sci. Univ. Aarhus, Aarhus, Denmark, (2002)
Y. Shi, R. C. Eberhart, A modified particle swarm optimizer, Proc. Conference Evol. Comput., IEEE Press, Piscataway, (1998), 69–73.
P. N. Suganthan, Particle swarm optimizer with neighbourhood operator , IEEE Congress Evol. Comput., (1999), 1958– 1962.
K. Veeramachaneni, T. Peram, C. Mohan, L. A. Osadciw, Optimization using particle swarms with near neighbor interactions, Proc. Genetic Evol. Comput. Conference (GECCO), Berlin, (2003), 110–121.
H. Wang, H. Li, Y. Liu, C. H. Li, S. Y. Zeng, Opposition-based particle swarm algorithm with Cauchy mutation, Proc. Conference Evol. Comput., (2007), 4750–4756.