Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems

Iranian Journal of Optimization

مقاله 2، دوره 11، شماره 1، شهریور 2019، صفحه 9-16   

نوع مقاله: Research Paper

نویسندگان

Hossein Towsyfyan¹؛ Amin Kolahdooz^{* 2}؛ Hazem Esmaeel³؛ Shahed Mohammadi⁴

¹Department of Mechanical Engineering, University of Huddersfield, Huddersfield, UK

²Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

³Department of Mechanical Engineering, University of Thi-qar, Nasiriyah, Iraq

⁴Department of Computer Science and Systems Engineering, Ayandegan University, Tonekabon, Iran

چکیده

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.

کلیدواژه‌ها

Noisy non-linear problems؛ GA؛ PSO

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