Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey

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,...

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Vydané v:IEEE transactions on evolutionary computation Ročník 19; číslo 5; s. 609 - 629
Hlavní autori: Tayarani-N., Mohammad-H., Yao, Xin, Xu, Hongming
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Calibration
Control systems
Engines
Evolution
Fault diagnosis
Fuels
Genetic algorithms
Heuristic
Optimization
Timing
multiobjective optimization
fault diagnosis
meta-heuristic algorithms
engine management systems
evolutionary algorithms (EAs)
Control system
engine calibration
memetic algorithms
ISSN:1089-778X, 1941-0026
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2014.2355174