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Performance and emission optimization of diesel engine by single and multi-objective genetic algorithms

Published: 17 June 2010 Publication History

Abstract

In this study, single and also multi-objective (MO) genetic algorithms (GAs) were used for optimisation of performance and emissions of a diesel engine. Population space and initial population of both GAs were obtained by Artificial Neural Network (ANN). Specific fuel consumption (Sfc), NOx, power (P), torque (Tq) and air-flow rate (Afr) were reduced to %7.7, %8.51, %30, %4 and %7.4 respectively whereas HC increased at the rate of %10.5 by traditional single objective GA. HC, CO2, P and Sfc were reduced to %17.6, %30.05, %31.8 and %14.5 respectively whereas NOx increased at the rate of %13 by using multi-objective GA with Nondominated Sorting Genetic Algorithm II (NSGA II). %14.5 fuel reduction against %31 power reduction have never been obtained in the previous studies. This shows the effective usage of MOGA with NSGA II in optimisation of fuel diesel engine performance parameters.

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  • (2015)Meta-Heuristic Algorithms in Car Engine Design: A Literature SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.235517419:5(609-629)Online publication date: Oct-2015

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CompSysTech '10: Proceedings of the 11th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing on International Conference on Computer Systems and Technologies
June 2010
575 pages
ISBN:9781450302432
DOI:10.1145/1839379
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 17 June 2010

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Author Tags

  1. ANN
  2. NSGA II
  3. diesel engine performance
  4. multi-objective genetic algorithm
  5. optimisation

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CompSysTech '10

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Overall Acceptance Rate 241 of 492 submissions, 49%

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  • (2015)Meta-Heuristic Algorithms in Car Engine Design: A Literature SurveyIEEE Transactions on Evolutionary Computation10.1109/TEVC.2014.235517419:5(609-629)Online publication date: Oct-2015

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