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Optimisation of double wishbone suspension system using multi-objective genetic algorithm
Published in
2010
Volume: 6457 LNCS
   
Pages: 445 - 454
Abstract
This paper presents an application of multi-objective optimisation for the design of an important component of automobiles, namely the suspension system. In particular, we focus on the double wishbone suspension, which is one of the most popular suspensions in use today and is commonly found on mid-range to high-end cars. The design of such mechanical systems is fairly complicated due to the large number of design variables involved, complicated kinematic model, and most importantly, multiplicity of design objectives, which show conflict quite often. The above characteristic of the design problem make it ideally suited for a study in optimisation using non-classical techniques for multi-objective optimisation. In this paper, we use +NSGA-II+ [5] for searching an optimal solution to the design problem. We focus on two important performance parameters, namely camber and toe, and propose objective functions which try to minimise the variation of these as the wheel travels in jounce and rebound. The pareto-optimal front between these two objectives are obtained using multiple formulations and their results are compared. © 2010 Springer-Verlag.
About the journal
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN03029743
Open AccessNo
Concepts (28)
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    CLASSICAL TECHNIQUES
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    DESIGN OBJECTIVES
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    Design problems
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    Design variables
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    DOUBLE WISHBONE SUSPENSION
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    Kinematic model
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    Mechanical systems
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    Multi-objective genetic algorithm
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    MULTIOBJECTIVE OPTIMISATION
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    NSGA-II
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    Objective functions
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    Optimal solutions
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    Optimisations
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    PARETO-OPTIMAL FRONT
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    Performance parameters
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    SUSPENSION SYSTEM
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    CLASSICAL TECHNIQUES
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    DOUBLE WISHBONES
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    PARETO-OPTIMAL FRONT
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    Automobile suspensions
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    Biology
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    KINEMATICS
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    Machine design
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    Multiobjective optimization
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    Suspensions (fluids)
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    Genetic algorithms
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    Pareto principle
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    Suspensions (components)