Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275088
Title: Preferences in evolutionary multiple criteria decision making optimisation
Author: Duenas, Alejandra
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2003
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Abstract:
Despite the number of approaches established for Multiple Criteria Optimisation Problems, few of them have been developed for the decision making process. This research work proposes a new methodology for the solution of optimisation problems that involve multiple criteria emphasising the Decision-Maker's (DM's) preferences model and the use of evolutionary computation techniques and fuzzy logic. The use of genetic algorithms (GAs) is of vital importance to the development of this research. The use of operations research (OR) techniques and decision analysis is also considered vital. The aim of this project is to provide a definition of hybrid approaches that combine the strengths of GA and decision analysis. For this reason four hybrid models are proposed: 1. The GA-SEMOPS. 2. The fuzzy multiobjective genetic optimiser. 3. The GA-PROTRADE. 4. The interactive procedure for multiple objective optimisation problems. The main characteristics of these approaches are that they handle the DM's preferences in an interactive way and their objective functions are formulated using goal levels and surrogate functions. In order to demonstrate that these models can be used in different optimisation problems they have been applied to different case studies covering examples from environmental systems to land and human resource allocation. Each model was studied in depth, comparing the results found with those available in literature. In the majority of the cases, it was found that they performed better than existing methods. The investigations carried out showed that the proposed hybrid models can be considered as a very powerful tool for the solution of a wide variety of optimisation problems in situations from business to science and engineering.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.275088  DOI: Not available
Keywords: Artificial intelligence Artificial intelligence
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