Particle Swarm Optimization : a tutorial

Tharwat, A; Gaber, T; Hassanien, AE; Elnaghi, BE

Abstract

Optimization algorithms are necessary to solve many problems such as parameter tuning. Particle Swarm Optimization (PSO) is one of these optimization algorithms. The aim of PSO is to search for the optimal solution in the search space. This paper highlights the basic background needed to understand and implement the PSO algorithm. This paper starts with basic definitions of the PSO algorithm and how the particles are moved in the search space to find the optimal or near optimal solution. Moreover, a numerical example is illustrated to show how the particles are moved in a convex optimization problem. Another numerical example is illustrated to show how the PSO trapped in a local minima problem. Two experiments are conducted to show how the PSO searches for the optimal parameters in one-dimensional and two-dimensional spaces to solve machine learning problems.

• Publication Date: Jan 1, 2017
• Deposit Date: Jun 24, 2021
• Publicly Available Date: Jun 24, 2021
• Pages: 614-635
• Series Title: Advances in Computational Intelligence and Robotics (ACIR)
• Book Title: Handbook of Research on Machine Learning Innovations and Trends
• ISBN: 9781522522294-(print);-9781522522300-(ebook)
• DOI: https://doi.org/10.4018/978-1-5225-2229-4.ch026
• Publisher URL: https://doi.org/10.4018/978-1-5225-2229-4.ch026
• Related Public URLs: https://doi.org/10.4018/978-1-5225-2229-4
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