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Particle Swarm Optimization: A Tutorial
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Author(s): Alaa Tharwat (Suez Canal University, Egypt & Cairo University, Egypt & Scientific Research Group in Egypt (SRGE), Egypt), Tarek Gaber (University of Manchester, UK, and Suez Canal University, Egypt), Aboul Ella Hassanien (Cairo University, Egypt)and Basem E. Elnaghi (Suez Canal University, Egypt)
Copyright: 2017
Pages: 22
Source title:
Handbook of Research on Machine Learning Innovations and Trends
Source Author(s)/Editor(s): Aboul Ella Hassanien (Cairo University, Egypt)and Tarek Gaber (Suez Canal University, Egypt)
DOI: 10.4018/978-1-5225-2229-4.ch026
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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.
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