Open Access
Review
| Issue |
Int. J. Simul. Multidisci. Des. Optim.
Volume 11, 2020
|
|
|---|---|---|
| Article Number | 5 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/smdo/2019022 | |
| Published online | 31 January 2020 | |
- J. Kennedy, R. Eberhart, Particle swarm optimization, in Proceedings of the IEEE International Joint Conference on Neural Networks, IEEE Press, vol. 8, 1995, pp. 1943–1948 [Google Scholar]
- J. Kennedy, R. Mendes, Population structure and particle swarm performance, in Proceedings of the IEEE 2002 Congress on Evolutionary Computation, 2002 [Google Scholar]
- T.-O. Ting, M.V.C. Rao, C.K. Loo, S.-S. Ngu, A new class of operators to accelerate particle swarm optimization, Proc. IEEE Congr. Evol. Comput. 4, 2406–2410 (2003) [Google Scholar]
- R. Poli, W.B. Langdon, O. Holland, Extending particle swarm optimization via genetic programming, in: M. Keijzer, A.G.B. Tettamanzi, P. Collet, J. van Hemert, M. Tomassini (Eds.), Genetic Programming, LNCS (Springer, Heidelberg, 2005), vol. 3447, pp. 291–300 [CrossRef] [Google Scholar]
- T.R. Ramanan, M. Iqbal, K. Umarali. A particle swarm optimization approach for permutation flow shop scheduling problem, Int. J. Simul. Multisci. Des. Optim. 5, A20 (2014) [CrossRef] [Google Scholar]
- N. Elhami, R. Ellaia, M. Itmi, Hybrid evolutionary optimization algorithm MPSO-SA, Int. J. Simul. Multisci. Des. Optim. 4, 27–32 (2010) [CrossRef] [Google Scholar]
- M. Zemzami, N. Elhami, M. Itmi, N. Hmina, A new parallel approach for the exploitation of the search space based on PSO algorithm, in International Colloquium in Information Science and Technology (CIST'16), Tangier, Morocco, 2016 [Google Scholar]
- J. Chang, S. Chu, J. Roddick, J. Pan, A parallel particle swarm optimization algorithm with communication strategies, J. Inf. Sci. Eng. 21, 809–818 (2005) [Google Scholar]
- K. Byung-I, G. Alan, Parallel asynchronous particle swarm optimization, Int. J. Numer. Methods Eng. 67, 578–595 (2006) [CrossRef] [Google Scholar]
- M. Rashid, A. Rauf Baig, PSOGP: A genetic programming based adaptable evolutionary hybrid particle swarm optimization, Int. J. Innov. Comput. Inf. Control 6, 287–296 (2010) [Google Scholar]
- P. Rabanal, I. Rodríguez, F. Rubio, Parallelizing particle swarm optimization in a functional programming environnement, Algorithms 7, 554–581 (2014) [CrossRef] [Google Scholar]
- M. Zemzami, N. Elhami, M. Itmi, N. Hmina, Parallèlisation de la Méthode PSO: découpage de l'espace et traitement par lot des particules, in International Workshop on New Services and Networks (WNSN'16), Khouribga, Morocco, 2016 [Google Scholar]
- M.I. Aouad, Conception d'algorithmes hybrides pour l'optimisation de l'énergie mémoire dans les systèmes embarqués et de fonctions multimodales, Doctorat thesis, Henri Poincaré University- Nancy 1, France, 2011 [Google Scholar]
- M.E. Hyass, P. Hyass, Good parameters for particle swarm optimization, in: Laboratories Technical Report No. HL 1001, 2010 [Google Scholar]
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