On Particle Swarm Optimization Algorithm
DOI:
https://doi.org/10.31185/wjps.251Abstract
The swarm particle optimization algorithm is among the most important tools in finding the optimal solution to nonlinear optimization problems. The main goal of this research is an expanded study by developing an effective algorithm to find the optimal solution based on the speed of convergence. The study also included comparing the results with an algorithm with the same orientation. The results showed the superiority of the developed algorithm based on the results obtained
References
Edwin KP Chong and Stanislaw H Zak. An introduction to optimization, volume 75.
John Wiley & Sons, 2013.
Ibrahim Dincer, Marc A Rosen, and Pouria Ahmadi. Optimization of energy systems.
John Wiley & Sons, 2017.
Andreas Antoniou and Wu-Sheng Lu. Practical optimization. Springer, 2007.
Malick, J. (2007). The spherical constraint in boolean quadratic programs. Journal of Global Optimization, 39(4), 609-622. Journal of Global Optimization, 39:609–622, 2007. Wenyu Sun and Ya-Xiang Yuan. Opti-mization theory and methods: nonlinear Programming, volume 1. Springer Science & Business Media, 2006
Wenyu Sun and Ya-Xiang Yuan. Optimization theory and methods: nonlinear
Programming, volume 1. Springer Science & Business Media, 2006.
] Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. Convex optimization.
Cambridge university press, 2004.
Xin-She Yang. Introduction to algorithms for data mining and machine learning.
Academic press, 2019.
Lorenz T Biegler, Omar Ghattas, Matthias Heinkenschloss, David Keyes, and Bart
Van Bloemen Waanders. Real-time PDE-constrained Optimization. SIAM, 2007.
George Bernard Dantzig. Linear Programming and Extensions. Princeton University
Press, 1998.
Zhi-Hua Zhou. Machine learning. Springer Nature, 2021.
R Clark Robinson. Introduction to mathematical optimization. Department of
Mathematics, Northwestern University, Illinois US, 2013.
Jan A Snyman and Daniel N Wilke. Practical Mathematical Optimization: Basic
Optimization Theory and Gradient-Based Algorithms, volume 133. Springer, 2018.
Rajesh Kumar Arora. Optimization: algorithms and applications. CRC press, 2015.
Xin-She Yang. Optimization and metaheuristic algorithms in engineering.
Metaheuristics in water, geotechnical and transport engineering, 1:23, 2013.
Leslie R Foulds. Optimization techniques: an introduction. Springer Science &
Business Media, 2012.
Particle swarm optimization (PSO) algorithm is a population based heuristic global optimization technology introduced by Kennedy and Eberhart [1] in 1995, and referred to as a swarm-intelligence technique. Its basic idea is based on the simulation of simplified animal social behaviors, such as fish schooling, bird flocking, etc.
Amudha, P., Karthik, S., & Sivakumari, S. (2015). A hybrid swarm intelligence algo-rithm for intrusion detection using significant features. The Scientific World Journal, 2015.
Venter, G., & Sobieszczanski-Sobieski, J. (2004). Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization. Structural and Multidisciplinary optimization, 26, 121-131.
Gad, A. G. (2022). Particle swarm optimization algorithm and its applications: a sys-tematic review. Archives of computational methods in engineering, 29(5), 2531-2561.
Zhang, Y., Wang, S., & Ji, G. (2015). A comprehensive survey on particle swarm op-timization algorithm and its applications. Mathematical problems in engineering, 2015.
Blum, C., & Li, X. (2008). Swarm intelligence in optimization. In Swarm intelligence: introduction and applications (pp. 43-85). Berlin, Heidelberg: Springer Berlin Heidelberg.
Jau, Y. M., Su, K. L., Wu, C. J., & Jeng, J. T. (2013). Modified quantum-behaved particle swarm optimization for parameters estimation of generalized nonlinear multi-regressions model based on Choquet integral with outliers. Applied Mathematics and Com-putation, 221, 282-295.
Davoodi, E., Hagh, M. T., & Zadeh, S. G. (2014). A hybrid Improved Quantum-behaved Particle Swarm Optimization–Simplex method (IQPSOS) to solve power system load flow problems. Applied Soft Computing, 21, 171-179.
Y. Zhang, D.-W. Gong, and Z. Ding, “A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch,” Information Sciences, vol. 192, pp. 213–227, 2012.
Q. Zhang, Z. Li, C. J. Zhou, and X. P. Wei, “Bayesian network structure learning based on the chaotic particle swarm optimization algorithm,” Genetics and Molecular Research, vol. 12, no. 4, pp. 4468–4479, 2013.
C.-H. Yang, Y.-D. Lin, L.-Y. Chuang, and H.-W. Chang, “Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interac-tions,” BioMed Research International, vol. 2014, Article ID 172049, 10 pages, 2014.
Brownlee, J. (2016). Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery.
Kuo, R. J., & Hong, C. W. (2013). Integration of genetic algorithm and particle swarm optimization for investment portfolio optimization. Applied mathematics & information sciences, 7(6), 2397.
Ghamisi, P., & Benediktsson, J. A. (2014). Feature selection based on hybridization of genetic algorithm and particle swarm optimization. IEEE Geoscience and remote sensing letters, 12(2), 309-313.
M. Kanemasa and E. Aiyoshi, “Algorithm tuners for PSO methods and genetic pro-gramming techniques for learning tuning rules,” IEEJ Transactions on Electrical and Elec-tronic Engineering, vol. 9, no. 4, pp. 407–414, 2014.
Kanemasa, M., & Aiyoshi, E. (2014). Algorithm tuners for PSO methods and genetic programming techniques for learning tuning rules. IEEJ Transactions on Electrical and Electronic Engineering, 9(4), 407-414.
Zhang, Y., Balochian, S., Agarwal, P., Bhatnagar, V., & Housheya, O. J. (2014). Artifi-cial intelligence and its applications. Mathematical problems in Engineering, 2014.
Mahdavi, S., Rahnamayan, S., & Deb, K. (2018). Opposition based learning: A litera-ture review. Swarm and evolutionary computation, 39, 1-23.
Shahzad, F., Masood, S., & Khan, N. K. (2014). Probabilistic opposition-based particle swarm optimization with velocity clamping. Knowledge and information systems, 39, 703-737.
Zhang, Y. D., Wang, S., & Dong, Z. (2014). Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progress In Electromagnetics Research, 144, 171-184.
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