Author:
Guillaume Sandou
Affiliation:
Supélec, France
Keyword(s):
Metaheuristics, unit commitment, ant colony, genetic algorithm, scheduling.
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 cooperative metaheuristic for the solution of the Unit Commitment problem is presented. This problem is known to be a large scale, mixed integer problem. Due to combinatorial complexity, the exact solution is often intractable. Thus, a metaheuristic based method has to be used to compute a near optimal solution with low computation times. A new approach is presented here. The main idea is to couple a genetic algorithm to compute binary variables (on/off status of units), and an ant colony based algorithm to compute real variables (produced powers). Finally, results show that the cooperative method leads to the tractable computation of a satisfying solution for medium scale Unit Commitment problems.