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

Mohammadhassan Tayaraninajaran, Xin Yao, Hongming Xu

Research output: Contribution to journalArticlepeer-review

52 Citations (Scopus)
54 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)609 - 629
JournalIEEE Transactions on Evolutionary Computation
Issue number5
Publication statusPublished - 5 Sept 2014


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