Meta-heuristic algorithms in car engine design: a literature survey

Tayarani-N, M.-H., Yao, X. and Xu, H. (2015) Meta-heuristic algorithms in car engine design: a literature survey. IEEE Transactions on Evolutionary Computation, 19(5), pp. 609-629. (doi: 10.1109/TEVC.2014.2355174)

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Abstract

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system.

Item Type:Articles
Additional Information:This work was supported in part by EPSRC Grant EP/J00930X/1 and in part by NSFC Grant 61329302. The work of X. Yao was supported by Royal Society Wolfson Research Merit Award.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Tayarani, Dr Mohammad
Authors: Tayarani-N, M.-H., Yao, X., and Xu, H.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:IEEE Transactions on Evolutionary Computation
Publisher:IEEE
ISSN:1089-778X
Published Online:05 September 2014
Copyright Holders:Copyright © 2015 IEEE
First Published:First published in IEEE Transactions on Evolutionary Computation 19(5): 609-629
Publisher Policy:Reproduced under a Creative Commons License

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