Updating the velocity in particle swarm optimization (PSO) consists of three terms: the inertia term, the cognitive term and the social term. The balance of these terms determines the balance of the global and local search abilities, and therefore the performance of PSO. In this work, an adaptive parallel PSO algorithm, which is based on the dynamic exchange of control parameters between adjacent swarms, has been developed. The proposed PSO algorithm enables us to adaptively optimize inertia factors, learning factors and swarm activity. By performing simulations of a search for the global minimum of a benchmark multimodal function, we have found that the proposed PSO successfully provides appropriate control parameter values, and thus good global optimization performance.
Kennedy, J. and Eberhart, R.C. (1995) Particle Swarm Optimization. Proceeding of IEEE International Conference on Neural Networks, 4, 1942-1948.
http://dx.doi.org/10.1109/ICNN.1995.488968
Janson, S. and Middendorf, M. (2005) A Hierarchical Particle Swarm Optimizer and Its Adaptive Variant. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 35, 1272- 1282. http://dx.doi.org/10.1109/TSMCB.2005.850530
Yen, G.G. and Daneshyari, M. (2006) Diversity-Based Information Exchange among Multiple Swarms in Particle Swarm Optimization. Proceeding of IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, 16-21 July 2006, 6150-6157.
http://dx.doi.org/10.1109/cec.2006.1688511
Eberhart, R.C. and Shi, Y. (2000) Comparing Inertia Weights and Constrictions Factors in Particle Swarm Optimization. Proceeding of the Congress on Evolutionary Computation (CEC 2000), San Diego, 16-19 July 2000, 84-88. http://dx.doi.org/10.1109/cec.2000.870279
Clerc, M. and Kennedy, J. (2002) The Particle Swarm: Explosion, Stability, and Convergence in a Multi-Dimensional Complex Space. IEEE Transactions on Evolutionary Computation, 6, 58-73. http://dx.doi.org/10.1109/4235.985692
Cooren, Y., Clerc, M. and Siarry, P. (2009) Performance Evaluation of TRIBES, an Adaptive Particle Swarm Optimization Algorithm. Swarm Intelligence, 3, 149-178.
http://dx.doi.org/10.1007/s11721-009-0026-8
Jana, N.D. and Sil, J. (2014) Particle Swarm Optimization with Lévy Flight and Adaptive Polynomial Mutation in Gbest Particle. Recent Advances in Intelligent Informatics, 235, 275-282. http://dx.doi.org/10.1007/978-3-319-01778-5_28
Hukushima, K. and Nemoto, K. (1996) Exchange Monte Carlo Method and Application to Spin Glass Simulations. Journal of the Physical Society of Japan, 65, 1604-1608.
http://dx.doi.org/10.1143/JPSJ.65.1604
Shi, Y. and Eberhart, R.C. (1998) Parameter Selection in Particle Swarm Optimization, Evolutionary Programming VII. Lecture Notes in Computer Science, 1447, 591-600.
http://dx.doi.org/10.1007/BFb0040810
Li, H.-R. and Gao, Y.-L. (2009) Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation. Proceeding of 2009 2nd International Conference on Information and Computing Science, 1, 66-69.
http://dx.doi.org/10.1109/ICIC.2009.24
Xin, J., Chen, G. and Hai, Y. (2009) A Particle Swarm Optimizer with Multi-Stage Linearly-Decreasing Inertia Weight. Proceeding of the 2009 International Joint Conference on Computational Sciences, and Optimization, 1, 505-508.
http://dx.doi.org/10.1109/CSO.2009.420
Yasuda, K., et al. (2008) Particle Swarm Optimization: A Numerical Stability Analysis and Parameter Adjustment Based on Swarm Activity. IEEE Transactions on Electrical and Electronic Engineering, 3, 642-659. http://dx.doi.org/10.1002/tee.20326
