Meta-heuristic Algorithms in Car Engine Design: a Literature Survey
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- @Article{Tayarani:2014:ieeeTEC,
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author = "Mohammad-H. Tayarani-N. and Xin Yao and Hongming Xu",
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journal = "IEEE Transactions on Evolutionary Computation",
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title = "Meta-heuristic Algorithms in Car Engine Design: a
Literature Survey",
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year = "2015",
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volume = "19",
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number = "5",
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month = oct,
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pages = "609--629",
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note = "Accepted",
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keywords = "genetic algorithms, genetic programming, DE, ES, EDA,
AIS, Calibration, Control systems, Engines, Fuels,
Optimisation, Timing",
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DOI = "doi:10.1109/TEVC.2014.2355174",
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ISSN = "1089-778X",
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size = "21 pages",
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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 behaviours
in biology, flock behaviours of some birds, annealing
in metallurgy, etc. Due to their great potential in
solving hard optimisation problems, metaheuristic
algorithms have found their ways into automobile engine
design. There are different optimisation problems
arising in different areas of car engine management
including calibration, control system, fault diagnosis
and modelling. In this paper we review the
state-of-the-art applications of different
metaheuristic algorithms in engine management systems.
The review covers a wide range of research, including
the application of meta-heuristic algorithms in engine
calibration, optimising engine control systems, engine
fault diagnosis, optimising different parts of engines
and modelling. 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 optimisation,
particle swarm optimisation, memetic algorithms, and
artificial immune system.",
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notes = "Also known as \cite{6893031}",
- }
Genetic Programming entries for
Mohammad-H Tayarani-N
Xin Yao
Hongming Xu
Citations