### Abstract

other members. An algorithm simulating the intensity of the light of a single firefly, diminishing with increasing distances, is implicitly able to detect local solutions on its way to the best solution in the search space. This implicit clustering feature is stressed by an additional explicit clustering step, where local solutions are stored and terminally processed to

obtain a large number of possible solutions. The enhanced firefly algorithm will be first applied to the well-known Rastrigin functions and then to the tyre parametrisation problem. It is shown that the firefly algorithm is qualified to find a high number of optimisation solutions, which is required for plausible parametrisation for the given tyre model.

Original language | English |
---|---|

Number of pages | 11 |

Journal | Advances in Mechanical Engineering |

Volume | Vol. 9(1) |

Publication status | Published - 10 Jan 2017 |

### Keywords

### Cite this

**Parameterisation of a Maxwell model for transient tyre force by means of an extended firefly algorithm.** / Hackl, Andreas; Hirschberg, Wolfgang; Lex, Cornelia; Magele, Christian.

Research output: Contribution to journal › Article › Research › peer-review

}

TY - JOUR

T1 - Parameterisation of a Maxwell model for transient tyre force by means of an extended firefly algorithm

AU - Hackl, Andreas

AU - Hirschberg, Wolfgang

AU - Lex, Cornelia

AU - Magele, Christian

PY - 2017/1/10

Y1 - 2017/1/10

N2 - Developing functions for advanced driver assistance systems requires very accurate tyre models, especially for the simulation of transient conditions. In the past, parametrisation of a given tyre model based on measurement data showed shortcomings, and the globally optimal solution obtained did not appear to be plausible. In this article, an optimisation strategy is presented, which is able to find plausible and physically feasible solutions by detecting many local outcomes. The firefly algorithm mimics the natural behaviour of fireflies, which use a kind of flashing light to communicate withother members. An algorithm simulating the intensity of the light of a single firefly, diminishing with increasing distances, is implicitly able to detect local solutions on its way to the best solution in the search space. This implicit clustering feature is stressed by an additional explicit clustering step, where local solutions are stored and terminally processed toobtain a large number of possible solutions. The enhanced firefly algorithm will be first applied to the well-known Rastrigin functions and then to the tyre parametrisation problem. It is shown that the firefly algorithm is qualified to find a high number of optimisation solutions, which is required for plausible parametrisation for the given tyre model.

AB - Developing functions for advanced driver assistance systems requires very accurate tyre models, especially for the simulation of transient conditions. In the past, parametrisation of a given tyre model based on measurement data showed shortcomings, and the globally optimal solution obtained did not appear to be plausible. In this article, an optimisation strategy is presented, which is able to find plausible and physically feasible solutions by detecting many local outcomes. The firefly algorithm mimics the natural behaviour of fireflies, which use a kind of flashing light to communicate withother members. An algorithm simulating the intensity of the light of a single firefly, diminishing with increasing distances, is implicitly able to detect local solutions on its way to the best solution in the search space. This implicit clustering feature is stressed by an additional explicit clustering step, where local solutions are stored and terminally processed toobtain a large number of possible solutions. The enhanced firefly algorithm will be first applied to the well-known Rastrigin functions and then to the tyre parametrisation problem. It is shown that the firefly algorithm is qualified to find a high number of optimisation solutions, which is required for plausible parametrisation for the given tyre model.

KW - Vehicle Dynamics

KW - Tyre Dynamics Modelling

KW - Semi-Physical Tyre Model

KW - Parameter Optimisation

KW - Swarm Optimisation

KW - Firefly Algorithm

KW - Clustering

KW - Experimental Validation

M3 - Article

VL - Vol. 9(1)

JO - Advances in Mechanical Engineering

JF - Advances in Mechanical Engineering

SN - 1687-8140

ER -