Comparative Study of Particle Swarm Optimization and Genetic Algorithm Applied for Noisy Non-Linear Optimization Problems
محورهای موضوعی : Non linear Programming
Hossein Towsyfyan
1
,
امین کلاه دوز
2
,
Hazem Esmaeel
3
,
Shahed Mohammadi
4
1 - Department of Mechanical Engineering, University of Huddersfield, Huddersfield, UK
2 - دانشکده مهدسی مکانیک، دانشگاه آزاد اسلامی، واحد خمینی شهر، اصفهان، ایران
3 - Department of Mechanical Engineering, University of Thi-qar, Nasiriyah, Iraq
4 - Department of Computer Science and Systems Engineering, Ayandegan University, Tonekabon, Iran
کلید واژه: PSO, GA, Noisy non-linear problems,
چکیده مقاله :
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.
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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