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- W2012972688 endingPage "629" @default.
- W2012972688 startingPage "609" @default.
- W2012972688 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." @default.
- W2012972688 created "2016-06-24" @default.
- W2012972688 creator A5004791600 @default.
- W2012972688 creator A5017769188 @default.
- W2012972688 creator A5043925036 @default.
- W2012972688 date "2015-10-01" @default.
- W2012972688 modified "2023-10-01" @default.
- W2012972688 title "Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey" @default.
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