Development of a Genetic Algorithm Based Search Strategy Suited For Design Optimisation of Internal Combustion Engines

Simple item page
dc.contributor.authorNalitolela, Noel Gerald
dc.contributor.authorMshana, J. S.
dc.date.accessioned2016-07-14T20:43:34Z
dc.date.available2016-07-14T20:43:34Z
dc.date.issued2008
dc.description.abstractEngine design optimisation is a multi-objective, multi-domain problem in a discontinuous design space. The state of the art of optimisation techniques shows that only methods of direct and adaptive search are appropriate for this type of problem. These include, adaptive random search, simulated annealing, evolution strategies and genetic algorithms. Of these methods, the genetic algorithms have been shown to be the most suited for the optimisation of multi-modal response functions in a discontinuous design space. This paper considers the important characteristics of genetic algorithms and their adaptation for use in parametric design optimisation of internal combustion engines. In order to verify the basic functionality of the proposed optimisation strategy, a genetic algorithm based, optimisation software was developed and tested on a number of analytical functions, selected from optimisation literature, with satisfactory results.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11810/3187
dc.language.isoenen_US
dc.publisherTanzania Journal of Engineering and Technologyen_US
dc.subjectOptimisationen_US
dc.subjectEngine noiseen_US
dc.subjectMulti-objectiveen_US
dc.subjectMulti-domainen_US
dc.subjectGenetic algorithmen_US
dc.titleDevelopment of a Genetic Algorithm Based Search Strategy Suited For Design Optimisation of Internal Combustion Enginesen_US
dc.typeJournal Article, Peer Revieweden_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Development of a Genetic Algorithm Based Search Strategy Suited For Design Optimisation of Internal Combustion Engines.pdf
Size:
5.84 MB
Format:
Adobe Portable Document Format
Description:
Full Text
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Department of Mechanical and Industrial Engineering