
Development of a Genetic Algorithm Based Search Strategy Suited For Design Optimisation of Internal Combustion Engines
Author(s) -
Noel Gerald Nalitolela,
H. Kadete
Publication year - 2008
Publication title -
tanzania journal of engineering and technology/tanzania journal of engeering and technology
Language(s) - English
Resource type - Journals
eISSN - 1821-536X
pISSN - 2619-8789
DOI - 10.52339/tjet.v31i1.424
Subject(s) - simulated annealing , genetic algorithm , computer science , adaptive simulated annealing , mathematical optimization , parametric statistics , search algorithm , algorithm , machine learning , mathematics , statistics
Engine 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. Ofthese 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 basicfunctionality 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.