Short-term scheduling of thermal-electric generators using Lagrangian relaxation
Operations Research
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms
Selected Papers from AISB Workshop on Evolutionary Computing
Binary differential evolution for the unit commitment problem
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
An improved binary particle swarm optimization for unit commitment problem
Expert Systems with Applications: An International Journal
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
A biased random key genetic algorithm approach for unit commitment problem
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
A hyper-heuristic approach for the unit commitment problem
EvoCOMNET'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part II
A binary-real-coded differential evolution for unit commitment problem: a preliminary study
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Power law-based local search in differential evolution
International Journal of Computational Intelligence Studies
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The paper shows that the use of a memetic algorithm (MA), a genetic algorithm (GA) combined with local search, synergistically combined with Lagrangian relaxation is effective and efficient for solving large unit commitment problems in electric power systems. It is shown that standard implementations of GA or MA are not competitive with the traditional methods of dynamic programming (DP) and Lagrangian relaxation (LR). However, an MA seeded with LR proves to be superior to all alternatives on large problems. Eight problems from the literature and a new large, randomly generated problem are used to compare the performance of the proposed seeded MA with GA, MA, DP and LR. Compared with previously published results, this hybrid approach solves the larger problems better and uses less computational time.