Deb, K. (1999) Solving goal programming problems using multi-objective genetic algorithms Proceedings of Congress on Evolutionary Computation, 6-9 July (Washington DC, USA) . pp. 77-84.
|
PDF
- Publisher Version
835kB |
Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...
Related URL: http://dx.doi.org/10.1109/CEC.1999.781910
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. 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 a non-convex trade-off region, which are difficult to solve using classical methods. The efficacy of the proposed method is demonstrated by solving a number of test problems and by solving an engineering design problem. The results suggest that the proposed approach is a unique, effective, and practical tool for solving goal programming problems.
Item Type: | Article |
---|---|
Source: | Copyright of this article belongs to Proceedings of Congress on Evolutionary Computation, 6-9 July (Washington DC, USA). |
ID Code: | 81673 |
Deposited On: | 07 Feb 2012 05:22 |
Last Modified: | 18 May 2016 23:08 |
Repository Staff Only: item control page