A Seeded Memetic Algorithm for Large Unit Commitment Problems
Journal of Heuristics
Journal of Global Optimization
Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization
Journal of Heuristics
Binary differential evolution for the unit commitment problem
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Unit commitment problem using enhanced particle swarm optimization algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Bio-inspired Learning and Intelligent Systems
A deterministic annular crossover genetic algorithm optimisation for the unit commitment problem
Expert Systems with Applications: An International Journal
A real-integer-discrete-coded differential evolution algorithm: a preliminary study
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
A real-integer-discrete-coded differential evolution
Applied Soft Computing
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Due to its economical importance, the unit commitment problem has become a matter of concern in power systems, and consequently an important area of research. It is a nonlinear mixed-integer optimization problem, in which a given number of power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In this paper a binary-real-coded differential evolution along with some repairing mechanisms is investigated as the solution technique of the problem. In the computational experiment carried out with a hypothetical 10-unit power system over 24-hour time horizon, available in the literature, the proposed technique is found outperforming all the existing methods.