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

Research output: Contribution to journalArticlepeer-review

Colleges, School and Institutes


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 optimization, particle swarm optimization, memetic algorithms, and artificial immune system.


Original languageEnglish
Pages (from-to)609-629
Number of pages21
JournalIEEE Transactions on Evolutionary Computation
Issue number5
Early online date5 Sep 2014
Publication statusPublished - 1 Oct 2015


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