Towsyfyan, H., Kolahdooz, A., Esmaeel, H., Mohammadi, S. (2018). Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems. Iranian Journal of Optimization, (), -.
Hossein Towsyfyan; Amin Kolahdooz; Hazem Esmaeel; Shahed Mohammadi. "Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems". Iranian Journal of Optimization, , , 2018, -.
Towsyfyan, H., Kolahdooz, A., Esmaeel, H., Mohammadi, S. (2018). 'Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems', Iranian Journal of Optimization, (), pp. -.
Towsyfyan, H., Kolahdooz, A., Esmaeel, H., Mohammadi, S. Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems. Iranian Journal of Optimization, 2018; (): -.
Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems
Articles in Press, Accepted Manuscript , Available Online from 25 March 2018
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
Receive Date: 17 November 2017,
Revise Date: 26 January 2018,
Accept Date: 25 March 2018
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.
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