A novel combination of Particle Swarm Optimization and Genetic Algorithm for Pareto optimal design of a five-degree of freedom vehicle vibration model

Authors:
M. J. Mahmoodabadi;A. Adljooy Safaie;A. Bagheri;N. Nariman-Zadeh
Affiliations:
Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran;Department of Mechanical Engineering, Takestan Branch, Islamic Azad University, Takestan, Iran;Department of Mechanical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran;Intelligent-based Experimental Mechanics Center of Excellence School of Mechanical Engineering, Faculty of Engineering, University of Tehran, Tehran, Iran and Department of Mechanical Engineering, ...
Venue:
Applied Soft Computing
Year:
2013

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Abstract

In this paper, at first, a novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is introduced. This hybrid algorithm uses the operators such as mutation, traditional or classical crossover, multiple-crossover, and PSO formula. The selection of these operators in each iteration for each particle or chromosome is based on a fuzzy probability. The performance of the proposed hybrid algorithm for solving both single and multi-objective optimization problems is challenged by using of some well-known benchmark problems. Obtained numerical results are compared with those of other optimization algorithms. At the end, the proposed multi-objective hybrid algorithm is used for the Pareto optimal design of a five-degree of freedom vehicle vibration model. The comparison of the obtained results with it in the literature demonstrates the superiority of this work.