Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines

Knödler, Kosmas; Poland, Jan; Merz, Peter; Zell, Andreas

doi:10.1007/3-540-32363-5_5

Cited by 4 publications

(4 citation statements)

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“…The first group use a hybrid of a meta-heuristic algorithm with a local search or another type of meta-heuristic algorithms. For example, in [55]- [57], [94], [107], [109], [143], and [168], a meta-heuristic algorithm is hybridized with a local search algorithm. 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.…”

Section: Discussionmentioning

confidence: 99%

“…The second group are the works that improve the performance of the algorithm with operators. Improving crossover [56], [57] and mutation [62] operators, using history of search [80] and entropy in the individuals [58] to guide the individuals, preserving diversity among the individuals [148] and using niching strategies [172] are some examples of these works. The third group of works target the objective function.…”

Section: Discussionmentioning

confidence: 99%

“…Apart from using local search, the operators of genetic algorithms, like crossover, can also be manipulated to improve the performance of the algorithm. For example, in [56] and [57], it was shown that three process steps in engine calibration could be regarded as optimization problems that would benefit from genetic algorithms. The first step is the D-optimal experimental design, which is optimized with a local search algorithm and an appropriate crossover operator.…”

Section: A Control Unit Calibrationmentioning

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%

Section: Discussionmentioning

confidence: 99%

Section: A Control Unit Calibrationmentioning

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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“…The proposed MA integrates a genetic (Knodler et al, 2005) and simulated annealing (Bertsimas and Tsitsiklis, 1993) algorithm. The genetic algorithm was used for exploration; the simulated annealing algorithm was used for exploitation.…”

Section: Memetic Algorithmsmentioning

confidence: 99%

Calibration of traffic flow models using a memetic algorithm

Paz

Molano

Martinez

et al. 2015

Transportation Research Part C: Emerging Technologies

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a b s t r a c tA Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models is proposed in this study. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of the search space and identifies a zone where a possible global solution could be located. After this zone has been found, the simulated annealing algorithm refines the search and locates an optimal set of parameters within that zone. The design and implementation of this methodology seeks to enable the generalized calibration of microscopic traffic flow models. Two different Corridor Simulation (CORSIM) vehicular traffic systems were calibrated for this study. All parameters after the calibration were within reasonable boundaries. The calibration methodology was developed independently of the characteristics of the traffic flow models. Hence, it is easily used for the calibration of any other model. The proposed methodology has the capability to calibrate all model parameters, considering multiple performance measures and time periods simultaneously. A comparison between the proposed MA and the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm was provided; results were similar between the two. However, the effort required to finetune the MA was considerably smaller when compared to the SPSA. The running time of the MA-based calibration was larger when it was compared to the SPSA running time. The MA still required some knowledge of the model in order to set adequate optimization parameters. The perturbation of the parameters during the mutation process must have been large enough to create a measurable change in the objective function, but not too large to avoid noisy measurements.Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Modeling and control of diesel engines: A systematic review

Sujesh

Ramesh²

2018

Alexandria Engineering Journal

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Using Memetic Algorithms for Optimal Calibration of Automotive Internal Combustion Engines

Cited by 4 publications

References 10 publications

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

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

Calibration of traffic flow models using a memetic algorithm

Modeling and control of diesel engines: A systematic review

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