Authors: | Deb, Kalyanmoy |
Title: | Non-linear Goal Programming Using Multi-Objective Genetic Algorithms |
Language (ISO): | en |
Abstract: | Goal 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. |
URI: | http://hdl.handle.net/2003/5369 http://dx.doi.org/10.17877/DE290R-14973 |
Issue Date: | 2001-10-16 |
Provenance: | Universität Dortmund |
Appears in Collections: | Sonderforschungsbereich (SFB) 531 |
Files in This Item:
File | Description | Size | Format | |
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ci60.pdf | DNB | 4.48 MB | Adobe PDF | View/Open |
ci60.ps | 4.67 MB | Postscript | View/Open |
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