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

Mohammad-H.-N. TAYARANI, Xin YAO, Hongming XU

Research output: Journal PublicationsJournal Article (refereed)peer-review

81 Citations (Scopus)

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. © 1997-2012 IEEE.
Original languageEnglish
Article number6893031
Pages (from-to)609-629
Number of pages21
JournalIEEE Transactions on Evolutionary Computation
Volume19
Issue number5
Early online date17 Sept 2014
DOIs
Publication statusPublished - Oct 2015
Externally publishedYes

Bibliographical note

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 SocietyWolfson Research Merit Award.

Keywords

  • Control system
  • engine calibration
  • engine management systems
  • evolutionary algorithms (EAs)
  • fault diagnosis
  • memetic algorithms
  • meta-heuristic algorithms
  • multiobjective optimization

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