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dc.contributor.authorDeb, Kalyanmoyde
dc.description.abstractGoal programming is a technique often used in engineering design activities primarily to find a compromised solution which will simultaneously satisfy a number of design goals. In solving goal programming problems, classical methods reduce the multiple goal-attainment problem into a single objective of minimizing a weighted sum of deviations from goals. Moreover, in tackling non-linear goal programming problems, classical methods use successive linearization techniques, which are sensitive to the chosen starting solution. In this paper, we pose the goal programming problem as a multi-objective optimization problem of minimizing deviations from individual goals. This procedure eliminates the need of having extra constraints needed with classical formulations and also eliminates the need of any user-defined weight factor for each goal. The proposed technique can also solve goal programming problems having nonconvex trade-off region, which are difficult to solve using classical methods. The efficacy of the proposed method is demonstrated by solving a number of non-linear test problems and by solving an engineering design problem. The results suggest that the proposed approach is an unique, effective, and most practical tool for solving goal programming problems.en
dc.format.extent4584359 bytes-
dc.format.extent4786125 bytes-
dc.publisherUniversität Dortmundde
dc.relation.ispartofseriesReihe Computational Intelligence ; 60de
dc.titleNon-linear Goal Programming Using Multi-Objective Genetic Algorithmsen
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 531

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