Pareto optimization of a five-degree of freedom vehicle vibration model using a multi-objective uniform-diversity genetic algorithm (MUGA)

Authors:
N. Nariman-Zadeh;M. Salehpour;A. Jamali;E. Haghgoo
Affiliations:
Faculty of Mechanical Engineering, Islamic Azad University, Takestan Branch, Takestan, Iran and Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, P.O. Box 375 ...;Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, P.O. Box 3756, Rasht, Iran;Department of Mechanical Engineering, Faculty of Engineering, The University of Guilan, P.O. Box 3756, Rasht, Iran;Faculty of Mechanical Engineering, Islamic Azad University, Takestan Branch, Takestan, Iran
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 8
Cited 2

Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
Genetic diversity as an objective in multi-objective evolutionary algorithms

Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
Reliability-based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA)

Expert Systems with Applications: An International Journal
Performance scaling of multi-objective evolutionary algorithms

EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Probability of failure for uncertain control systems using neural networks and multi-objective uniform-diversity genetic algorithms (MUGA)

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

Applied Soft Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, a new multi-objective uniform-diversity genetic algorithm (MUGA) with a diversity preserving mechanism called the @e-elimination algorithm is used for Pareto optimization of a five-degree of freedom vehicle vibration model considering the five conflicting functions simultaneously. The important conflicting objective functions that have been considered in this work are, namely, seat acceleration, forward tire velocity, rear tire velocity, relative displacement between sprung mass and forward tire and relative displacement between sprung mass and rear tire. Further, different pairs of these objective functions have also been selected for 2-objective optimization processes. The comparison of the obtained results with those in the literature demonstrates the superiority of the results of this work. It is shown that the results of 5-objective optimization include those of 2-objective optimization and, therefore, provide more choices for optimal design of a vehicle vibration model.