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dc.contributor.authorCvetkovic, Dragan
dc.contributor.otherSchool of Engineering, Computing and Mathematicsen_US
dc.date.accessioned2011-09-22T10:56:02Z
dc.date.available2011-09-22T10:56:02Z
dc.date.issued2000
dc.identifierNot availableen_US
dc.identifier.urihttp://hdl.handle.net/10026.1/590
dc.descriptionMerged with duplicate record 10026.1/2328 on 07.20.2017 by CS (TIS)
dc.description.abstract

In this thesis the problem of conceptual engineering design and the possible use of adaptive search techniques and other machine based methods therein are explored. For the multi-objective optimisation (MOO) within conceptual design problem, genetic algorithms (GA) adapted to MOO are used and various techniques explored: weighted sums, lexicographic order, Pareto method with and without ranking, VEGA-like approaches etc. Large number of runs are performed for findingZ Dth e optimal configuration and setting of the GA parameters. A novel method, weighted Pareto method is introduced and applied to a real-world optimisation problem. Decision support methods within conceptual engineering design framework are discussed and a new preference method developed. The preference method for translating vague qualitative categories (such as "more important 91 , 4m.9u ch less important' 'etc. ) into quantitative values (numbers) is based on fuzzy preferences and graph theory methods. Several applications of preferences are presented and discussed: * in weighted sum based optimisation methods; s in weighted Pareto method; * for ordering and manipulating constraints and scenarios; e for a co-evolutionary, distributive GA-based MOO method; The issue of complexity and sensitivity is addressed as well as potential generalisations of presented preference methods. Interactive dynamical constraints in the form of design scenarios are introduced. These are based on a propositional logic and a fairly rich mathematical language. They can be added, deleted and modified on-line during the design session without need for recompiling the code. The use of machine-based agents in conceptual design process is investigated. They are classified into several different categories (e. g. interface agents, search agents, information agents). Several different categories of agents performing various specialised task are developed (mostly dealing with preferences, but also some filtering ones). They are integrated with the conceptual engineering design system to form a closed loop system that includes both computer and designer. All thesed ifferent aspectso f conceptuale ngineeringd esigna re applied within Plymouth Engineering Design Centre / British Aerospace conceptual airframe design project.

en_US
dc.description.sponsorshipBritish Aerospace Systems, Wartonen_US
dc.language.isoenen_US
dc.publisherUniversity of Plymouthen_US
dc.titleEvolutionary multi-objective decision support systems for conceptual designen_US
dc.typeThesis
dc.identifier.doihttp://dx.doi.org/10.24382/4130
dc.identifier.doihttp://dx.doi.org/10.24382/4130


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