Optimum design of run-flat tire insert rubber by genetic algorithm

Authors:
J. R. Cho;J. H. Lee;K. M. Jeong;K. W. Kim
Affiliations:
School of Mechanical Engineering, Pusan National University, Pusan 609-735, Republic of Korea and Research & Development Institute of Midas IT, Gyeonggi 463-400, Repubic of Korea;School of Mechanical Engineering, Pusan National University, Pusan 609-735, Republic of Korea;R&D Center of Kumho Tire Co. Ltd., Gwangju 500-757, Republic of Korea;R&D Center of Kumho Tire Co. Ltd., Gwangju 500-757, Republic of Korea
Venue:
Finite Elements in Analysis and Design
Year:
2012

Citing 9
Cited 1

A study of permutation crossover operators on the traveling salesman problem

Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Optimum spacing design of grillage systems using a genetic algorithm

Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Uniform Crossover in Genetic Algorithms

Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms and Highly Constrained Problems: The Time-Table Case

PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Approximate structural optimization using kriging method and digital modeling technique considering noise in sampling data

Computers and Structures
Optimum detailed design of reinforced concrete continuous beams using Genetic Algorithms

Computers and Structures
Crown shape optimization for enhancing tire wear performance by ANN

Computers and Structures

Multiobjective reliability-based optimization for design of a vehicledoor

Finite Elements in Analysis and Design

Quantified Score

Hi-index	0.00

Visualization

Abstract

A generalized multi-objective optimization method making use of genetic algorithm (GA) is introduced, in order to simultaneously improve the riding comfort and the durability of run-flat tire by optimally tailoring the shape and stiffness of the sidewall insert rubber. The sensitivity analysis invoking the CPU time-consuming finite element analyses is replaced with the genetic evolution and the fitness of each genome in the population is evaluated by utilizing the response surfaces of objective functions approximated by ANN. It is confirmed through the numerical experiment that a number of Pareto solutions of the shape and stiffness of the sidewall insert rubber for different combinations of weighting factors can be successfully obtained. As well, the reliability of the Pareto solutions has been justified from the comparison with the direct finite element analysis.