University of Hertfordshire

By the same authors

Improving the performance of evolutionary engine calibration algorithms with principal component analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

Improving the performance of evolutionary engine calibration algorithms with principal component analysis. / Tayarani, Mohammad; Prugel-Bennett, Adam; Yao, Xin; Xu, Hongming.

2016 IEEE Congress on Evolutionary Computation (CEC). 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Tayarani, M, Prugel-Bennett, A, Yao, X & Xu, H 2016, Improving the performance of evolutionary engine calibration algorithms with principal component analysis. in 2016 IEEE Congress on Evolutionary Computation (CEC).

APA

Tayarani, M., Prugel-Bennett, A., Yao, X., & Xu, H. (2016). Improving the performance of evolutionary engine calibration algorithms with principal component analysis. In 2016 IEEE Congress on Evolutionary Computation (CEC)

Vancouver

Tayarani M, Prugel-Bennett A, Yao X, Xu H. Improving the performance of evolutionary engine calibration algorithms with principal component analysis. In 2016 IEEE Congress on Evolutionary Computation (CEC). 2016

Author

Tayarani, Mohammad ; Prugel-Bennett, Adam ; Yao, Xin ; Xu, Hongming. / Improving the performance of evolutionary engine calibration algorithms with principal component analysis. 2016 IEEE Congress on Evolutionary Computation (CEC). 2016.

Bibtex

@inproceedings{311a21d4cf3f45c8b86dc7740182c9ad,
title = "Improving the performance of evolutionary engine calibration algorithms with principal component analysis",
abstract = "By studying the fitness landscape properties of engine calibration problem we propose a new Principal Component Analysis (PCA) based optimisation algorithm for the problem. The engine calibration problem in this paper is to minimise the fuel consumption, gas emission and particle emission of a Jaguar car engine. To evaluate the fuel consumption and emissions of the engine, a model of the engine that was developed in University of Birmingham was used. A strength Pareto method is used to convert the three objectives into one fitness value. Then a local search algorithm is used to find local optima. We then study these local optima to find the properties of good solutions in the landscape. Our studies on the good solutions show that the best solutions in the landscape show some patterns. We perform Principal Component Analysis (PCA) on the good solutions and show that these components present certain properties, which can be exploited to develop new exploration operators for evolutionary algorithms. We use the newly proposed operator on some well-known algorithms and show that the performance of the algorithms can be improved significantly.",
author = "Mohammad Tayarani and Adam Prugel-Bennett and Xin Yao and Hongming Xu",
year = "2016",
month = sep,
day = "3",
language = "English",
booktitle = "2016 IEEE Congress on Evolutionary Computation (CEC)",

}

RIS

TY - GEN

T1 - Improving the performance of evolutionary engine calibration algorithms with principal component analysis

AU - Tayarani, Mohammad

AU - Prugel-Bennett, Adam

AU - Yao, Xin

AU - Xu, Hongming

PY - 2016/9/3

Y1 - 2016/9/3

N2 - By studying the fitness landscape properties of engine calibration problem we propose a new Principal Component Analysis (PCA) based optimisation algorithm for the problem. The engine calibration problem in this paper is to minimise the fuel consumption, gas emission and particle emission of a Jaguar car engine. To evaluate the fuel consumption and emissions of the engine, a model of the engine that was developed in University of Birmingham was used. A strength Pareto method is used to convert the three objectives into one fitness value. Then a local search algorithm is used to find local optima. We then study these local optima to find the properties of good solutions in the landscape. Our studies on the good solutions show that the best solutions in the landscape show some patterns. We perform Principal Component Analysis (PCA) on the good solutions and show that these components present certain properties, which can be exploited to develop new exploration operators for evolutionary algorithms. We use the newly proposed operator on some well-known algorithms and show that the performance of the algorithms can be improved significantly.

AB - By studying the fitness landscape properties of engine calibration problem we propose a new Principal Component Analysis (PCA) based optimisation algorithm for the problem. The engine calibration problem in this paper is to minimise the fuel consumption, gas emission and particle emission of a Jaguar car engine. To evaluate the fuel consumption and emissions of the engine, a model of the engine that was developed in University of Birmingham was used. A strength Pareto method is used to convert the three objectives into one fitness value. Then a local search algorithm is used to find local optima. We then study these local optima to find the properties of good solutions in the landscape. Our studies on the good solutions show that the best solutions in the landscape show some patterns. We perform Principal Component Analysis (PCA) on the good solutions and show that these components present certain properties, which can be exploited to develop new exploration operators for evolutionary algorithms. We use the newly proposed operator on some well-known algorithms and show that the performance of the algorithms can be improved significantly.

M3 - Conference contribution

BT - 2016 IEEE Congress on Evolutionary Computation (CEC)

ER -