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

[Display omitted] ► A novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is introduced. ► 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...

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Bibliographic Details
Published in:Applied soft computing Vol. 13; no. 5; pp. 2577 - 2591
Main Authors: Mahmoodabadi, M.J., Safaie, A. Adljooy, Bagheri, A., Nariman-zadeh, N.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2013
Subjects:
Genetic Algorithm
Hybrid algorithms
Multi-objective problems
Particle Swarm Optimization
Single-objective problems
Vehicle vibration model
Hybrid algorithms
Multi-objective problems
Vehicle vibration model
Single-objective problems
Particle Swarm Optimization
Genetic Algorithm
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:[Display omitted] ► A novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is introduced. ► 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. ► The proposed multi-objective hybrid algorithm is used to Pareto optimal design of a five-degree of freedom vehicle vibration model. 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.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2012.11.028