Meta-heuristic Algorithms in Car Engine Design: a Literature Survey

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Tayarani:2014:ieeeTEC,
  author =       "Mohammad-H. Tayarani-N. and Xin Yao and Hongming Xu",
  journal =      "IEEE Transactions on Evolutionary Computation",
  title =        "Meta-heuristic Algorithms in Car Engine Design: a
                 Literature Survey",
  year =         "2015",
  volume =       "19",
  number =       "5",
  month =        oct,
  pages =        "609--629",
  note =         "Accepted",
  keywords =     "genetic algorithms, genetic programming, DE, ES, EDA,
                 AIS, Calibration, Control systems, Engines, Fuels,
                 Optimisation, Timing",
  DOI =          "doi:10.1109/TEVC.2014.2355174",
  ISSN =         "1089-778X",
  size =         "21 pages",
  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.",
  notes =        "Also known as \cite{6893031}",
}

Genetic Programming entries for Mohammad-H Tayarani-N Xin Yao Hongming Xu

Citations