Title of article
Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems
Author/Authors
Towsyfyan, Hossein Department of Mechanical Engineering - University of Huddersfield - Huddersfield , UK , Kolahdooz, Amin Department of Mechanical Engineering - Khomeinishahr Branch - Islamic Azad University , Esmaeel, Hazem Department of Mechanical Engineering - University of Thi-Qar - Nasiriyah , Iraq , Mohammadi, Shahed Department of Computer Science and Systems Engineering - Ayandegan University - Tonekabon
Pages
8
From page
9
To page
16
Abstract
Optimization of noisy non-linear problems plays a key role in
engineering and design problems. These optimization problems can't
be solved effectively by using conventional optimization methods.
However, metaheuristic algorithms such as Genetic Algorithm (GA)
and Particle Swarm Optimization (PSO) seem very efficient to
approach in these problems and became very popular. The efficiency
of these methods against many new metaheuristic optimization algorithms
has been proved in previous works, however a robust comparison
between GA and PSO to solve noisy nonlinear problems has not
been reported yet. Therefore, in this paper GA and PSO are adapted
to find optimal solutions of some noisy mathematical models. Based
on the obtained results, GA shows a promising potential in terms of
number of iteration to converge and solutions found so far for either
for optimization of low or elevated levels of noise.
Keywords
noisy non-linear problems , GA , PSO
Journal title
Astroparticle Physics
Serial Year
2019
Record number
2436298
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