Evolutionary Optimization of Parametric Models: the Test Case of Combustion in a Diesel Engine

Farina, Marcello; Cesario, N.; Ruggiero, Davide; Amato, P.

doi:10.1007/3-540-32400-3_13

Cited by 2 publications

(2 citation statements)

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“…Thus, the global search advantages of the meta-heuristic algorithm and the hill climbing and speed of local search are combined to achieve a better algorithm. Other examples of hybrid algorithms are [60], [97], [107], [129], [130], [163], [182], and [190], where two types of meta-heuristic algorithms are combined so the advantages of one algorithm covers the weakness of the other one. The second group are the works that improve the performance of the algorithm with operators.…”

Section: Discussionmentioning

confidence: 99%

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

Tayarani-N.

Yao

2015

IEEE Trans. Evol. Computat.

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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, metaheuristic 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 metaheuristic 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.

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Section: Discussionmentioning

confidence: 99%

“…In order to build a parametric model of combustion in a combustion chamber of a diesel engine, differential evolution, and evolution strategies with different fitness definitions were studied in [190].…”

Section: ) Combustionmentioning

confidence: 99%