Grouping genetic operators for the delineation of functional areas based on spatial interaction
Expert Systems with Applications: An International Journal
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In this paper, an enhanced genetic algorithm for the Unit Commitmentproblem is presented. This problem is known to be a large scale, mixed integerprogramming problem for which exact solution is highly intractable.Thus, a metaheuristic based method has to be used to compute a very often suitablesolution. The main idea of the proposed enhanced genetic algorithm is touse a priori knowledge of the system to design new genetic operators so as toincrease the convergence rate. Further, a suitable penalty criterion is defined toexplicitly deal with numerous constraints of the problem and to guarantee thefeasibility of the solution. The method is also hybridized with an exact solutionalgorithm, which aims to compute real variables from integer variables. Finally,results show that the enhanced genetic algorithm leads to the tractable computationof a satisfying solution for large scale Unit Commitment problems.