Authors:
Guillaume Sandou
1
;
Stéphane Font
1
;
Sihem Tebbani
1
;
Arnaud Hiret
2
and
Christian Mondon
2
Affiliations:
1
Supelec, France
;
2
EDF Recherche et Développement, France
Keyword(s):
Metaheuristics, unit commitment, ant colony, genetic algorithm, feasibility.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Formal Methods
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Planning and Scheduling
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
Abstract:
In this paper, a new optimisation strategy for the solution of the classical Unit Commitment problem is proposed. This problem is known to be an often large scale, mixed integer programming problem. Due to high combinatorial complexity, the exact solution is often intractable. Thus, a metaheuristic based method has to be used to compute a very often suitable solution. The main idea of the approach is to use ant colony algorithm, to explicitly deal with the feasibility of the solution, and to feed a genetic algorithm whose goal is to intensively explore the search space. Finally, results show that the proposed method leads to the tractable computation of satisfying solutions for the Unit Commitment problem.