Abstract
In this paper, an enhanced genetic algorithm for the Unit Commitment problem is presented. This problem is known to be a large scale, mixed integer programming problem for which exact solution is highly intractable. Thus, a metaheuristic based method has to be used to compute a very often suitable solution. The main idea of the proposed enhanced genetic algorithm is to use a priori knowledge of the system to design new genetic operators so as to increase the convergence rate. Further, a suitable penalty criterion is defined to explicitly deal with numerous constraints of the problem and to guarantee the feasibility of the solution. The method is also hybridized with an exact solution algorithm, which aims to compute real variables from integer variables. Finally, results show that the enhanced genetic algorithm leads to the tractable computation of a satisfying solution for large scale Unit Commitment problems.
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Sandou, G., Font, S., Tebbani, S., Hiret, A., Mondon, C. (2008). Enhanced Genetic Algorithm with Guarantee of Feasibility for the Unit Commitment Problem. In: Monmarché, N., Talbi, EG., Collet, P., Schoenauer, M., Lutton, E. (eds) Artificial Evolution. EA 2007. Lecture Notes in Computer Science, vol 4926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79305-2_25
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DOI: https://doi.org/10.1007/978-3-540-79305-2_25
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