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An effective genetic algorithm approach multiobjective resource allocation problems (MORAPs)
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AI
DOI：10.1016/j.amc.2003.10.057.png)
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Abstract
En 中文
Dynamic programming (DP) is a mathematical procedure designed primarily to improve the computational efficiency of solving select mathematical programming problems by decomposing them into smaller, and hence computationally simpler, subproblems. In solving Multiple objectives Dynamic Programming Problem (MODP), classical approaches reduce the multiple objectives into a single objective of minimizing a weighted sum of objectives. The determination of these weights indicate the relative importance of the various objective. Also, if the problem scale increases, it become difficult to be dealt with even in the case of single objective because of the rapid expansion of the number of states to be considered. In this paper, we investigated the possibility of using genetic algorithms (GAs) to solve multiobjective resource allocation problems (MORAPs). This procedure eliminates the need of any user defined weight factor for each objective. Also, the proposed approach is developed to deal with the problems with both single or multiple objectives. The simulation results for multiobjective resource allocation problems (MORAP) shows that genetic algorithms (GAs) may hopefully be a new approach for such kinds of problems. (c) 2004 Published by Elsevier Inc.
Keywords:
dynamic programming
multiobjective optimization
multiobjective resource allocation problem
genetic algorithms
Journal
IF:
3.4
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2.3W
Citations:
3.3W
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