Geodesic
Application of particle swarm method in the optimization problems
News of the Kabardin-Balkar scientific center of RAS, no. 5 (2022), pp. 48-57.

Voir la notice de l'article provenant de la source Math-Net.Ru

Particle swarm optimization (PSO) is a heuristic global optimization method originally proposed by J. Kennedy and R.K. Eberhart in 1995. Today, PSO is one of the best known and widely used swarm intelligence algorithms due to its simplicity and ability to be used in a wide range of applications. This paper provides an overview of PSO applications in various fields: electrical engineering, automatic control systems, medicine, biology, chemistry, etc.
Keywords: optimization, Particle Swarm Optimization.
Mots-clés : metaheuristic algorithm 
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E. M. Kazakova. Application of particle swarm method in the optimization problems. News of the Kabardin-Balkar scientific center of RAS, no. 5 (2022), pp. 48-57. http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a4/

[1] R. Eberhart, J. Kennedy, “Particle swarm optimization”, Proceedings of the IEEE International Conference on Neural Networks, 1995, no. 4, 1942-1948 | DOI

[2] R. Eberhart, J. Kennedy, “A new optimizer using particle swarm theory”, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, 39-43 | DOI

[3] C. W. Cleghorn, A. P. Engelbrecht, “Particle swarm convergence: An empirical investigation”, 2014 IEEE Congress on Evolutionary Computation (CEC), 2014, 2524-2530 | DOI

[4] A. Banks, J. Vincent, C. Anyakoha, “A review of particle swarm optimization. Part I: backgroundand development”, Nat. Comput., 4:6 (2007), 467-484 | DOI | MR

[5] A. P. Karpenko, E. Yu. Seliverstov, “A review of particle swarm methods for the global optimization problem (Particle Swarm Optimization)”, Mechanical Engineering and Computer Technologies, 2009, no. 3, 2 (in Russian) | MR

[6] E. H. Houssein, M. R. Saad, F. A. Hashim, H. Shaban, M. Hassaballah, “Levy flight distribution:a new metaheuristic algorithm for solving engineering optimization problems”, Eng. Appl. Artif. Intell, 94 (2020), 103731 | DOI

[7] P. Cazzaniga, M. S. Nobile, D. Besozzi, “The impact of particles initialization in PSO: parameter estimation as a case in point”, IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 94 (2015), 1–8 | DOI

[8] M. U. Farooq, A. Ahmad, A. Hameed, “Opposition-based initialization and a modified pattern for inertia weight (IW) in PSO”, IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), 2017, 96–101 | DOI

[9] J. Shang [et al.], “Hybrid Odor Detection System for Search and Rescue Robot Based on PSO”, Chemical Engineering Transactions, 68 (2018), 151–156

[10] A. Asma, B. Sadok, “PSO-based dynamic distributed algorithm for automatic task clustering in a robotic swarm”, Procedia Computer Science, 159 (2019), 1103–1112 | DOI

[11] Z. Bingul, O. Karahan, “Tuning of fractional PID controllers using PSO algorithm for robot trajectory control”, IEEE International Conference on Mechatronics, 2011, 955–960 | DOI

[12] K. Aurangzeb, S. Aslam, M. Alhussein, R. A. Naqvi, M. Arsalan, S. I. Haider, “Contrast Enhancement of Fundus Images by Employing Modified PSO for Improving the Performance of Deep Learning Models”, IEEE Access, 9 (2021), 47930–47945 | DOI

[13] Taijia Xiao, Dong Ren, Shuanghui Lei, Junqiao Zhang, Xiaobo Liu, “Based on grid-search and PSO parameter optimization for Support Vector Machine”, Proceeding of the 11th World Congress on Intelligent Control and Automation, 2014, 1529–1533 | DOI

[14] N. Zeng [et al.], “A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer's disease”, Neurocomputing, 320 (2018), 195–202 | DOI

[15] A. Dixit, A. Mani, R. Bansal, “CoV2-Detect-Net: Design of COVID-19 prediction model based on hybrid DE-PSO with SVM using chest X-ray images”, Information sciences, 571 (2021), 676–692 | DOI | MR

[16] S. O. Prakash, M. Jeyakumar, B. S. Gandhi, “Parametric optimization on electro chemical machining process using PSO algorithm”, Materials Today: Proceedings, 2022 | DOI

[17] V. Z. Manusov, P. V. Matrenin, H. Nasrullo, “Application of swarm intelligence algorithms in the management of a generating consumer with renewable energy sources”, Systems of Analysis and Data Processing, 76:3 (2019), 115–134 (in Russian) | DOI