Mathematical Model of Particle Swarm Optimization: Numerical Optimization Problems
Ashwin A. Kadkol
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Ashwin A. Kadkol: General Electric Research
Chapter Chapter 5 in Applying Particle Swarm Optimization, 2021, pp 73-95 from Springer
Abstract The Particle Swarm Optimization (PSO) algorithm was put forth by Kennedy and Eberhart in the year 1995. It is widely known for the ease with which it can be implemented and its simple approach. It is a multi-agent parallel search metaheuristic technique aimed at global optimization for numerical optimization problems. It has roots in artificial life techniques like swarm intelligence, fish schooling, etc. This chapter aims to introduce the mathematical bases for the algorithm and illustrates a few pictorial aids to understand the technique better. It is intended to serve as an introduction to spark the interest of the reader. Readers wishing to learn more about the applications of PSO and its variants to multi-objective, constrained, dynamic optimization problems and other advanced topics are recommended to consider the various references at the end of the chapter.
Keywords: Artificial Intelligence; Computational Intelligence; Swarm Intelligence; Evolutionary computation; Metaheuristics; Population heuristics; Bio-Inspired Algorithms (search for similar items in EconPapers)
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