Particle Swarm Optimization - Variants and Applications
Keywords:
Particle Swarm Optimization, Metaheuristic Algorithms, Swarm Intelligence, Optimization Techniques, Engineering ApplicationsAbstract
Particle Swarm Optimization (PSO) is a nature-inspired metaheuristic optimization algorithm that has gained significant attention due to its simplicity and effectiveness in solving complex optimization problems. This paper provides a comprehensive review of the variants and applications of PSO in both continuous and discrete optimization domains. We discuss the fundamental concepts of PSO, including the swarm intelligence and movement rules, and then delve into the various variants of PSO, such as adaptive PSO, chaotic PSO, and quantum-behaved PSO, highlighting their unique characteristics and advantages. Furthermore, we present a detailed overview of the diverse applications of PSO in engineering and science, including but not limited to, mechanical design optimization, power system optimization, image processing, and data clustering. Through this paper, we aim to provide researchers and practitioners with a thorough understanding of the capabilities and limitations of PSO, along with insights into its potential future developments.
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References
Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.
Eberhart, Russell C., and James Kennedy. "A New Optimizer Using Particle Swarm Theory." Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 69-73.
Kennedy, James. "Particle Swarm Optimization." Encyclopedia of Machine Learning, edited by Claude Sammut and Gareth Webb, Springer, 2011, pp. 760-766.
Clerc, Maurice. "Particle Swarm Optimization: Variants, Applications, and Hybridization Issues." IEEE Transactions on Evolutionary Computation, vol. 11, no. 1, 2007, pp. 25-51.
Poli, R. et al. "An Overview of Swarm Intelligence." Swarm Intelligence, edited by Marco Dorigo et al., Springer, 2008, pp. 1-32.
Liang, J. J., et al. "An Overview of Particle Swarm Optimization Variants." Journal of Computational Information Systems, vol. 8, no. 1, 2012, pp. 118-139.
Luenberger, David G. "Linear and Nonlinear Programming." International Series in Operations Research and Management Science, 3rd ed., Springer, 2008.
Coello Coello, Carlos Arturo Antonio. "Designing Optimization Algorithms with Evolutionary Techniques." Springer, 2000.
Mirjalili, Seyed Sareh. "Metaheuristics: Optimization Algorithms in Engineering and Science." Springer, 2016.
Engelbrecht, Arno P. "Computational Intelligence: An Introduction." Wiley, 2007.
Haupt, Randy L., and Susan E. Haupt. "Practical Optimization Algorithms." Wiley, 2004.
Venigandla, Kamala, et al. "Leveraging AI-Enhanced Robotic Process Automation for Retail Pricing Optimization: A Comprehensive Analysis." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2.2 (2023): 361-370.
Reddy, Surendranadha Reddy Byrapu. "Big Data Analytics-Unleashing Insights through Advanced AI Techniques." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 1-10.
Raparthi, Mohan, et al. "Data Science in Healthcare Leveraging AI for Predictive Analytics and Personalized Patient Care." Journal of AI in Healthcare and Medicine 2.2 (2022): 1-11.
