AI SearchAnt colony search algorithm for unit commitment
Call Number: AIT RSPR no. ET-02-10 Material type:
SeriesSeries: Asian Institute of Technology. Research studies project report ; no. ET-02-10Publication details: Bangkok : Asian Institute of Technology, 2002Description: 45 leavesSubject(s): Online resources: Summary: In this research study, Ant Colony Search Algorithm (ACSA) is proposed to solve thermal unit commitment problem. ACSA is a new co-operative agent approach, which is derived from the theoretical biology on the topic of ant trail formation and foraging methods. In the ACSA, a set of co-operating agents called "ants" co-operates to find good solution to unit commitment problem of thermal units. The merits of ACSA are parallel search and optimization capabilities. In this study, the problem is decomposed in two subproblems. The unit commitment sub-problem is solved by the ant colony search algorithm method and the economic dispatch sub-problem is solved by the lambda-iteration method. The total production cost of the proposed method is cheaper than those of Dynamic Programming (DP), Lagrange Relaxation (LR) and Genetic Algorithm (GA) on the ten generating units system.
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22-AIT Thesis (Replacement)
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40-Archives
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A research study submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Environment, Resources and Development
In this research study, Ant Colony Search Algorithm (ACSA) is proposed to solve thermal unit commitment problem. ACSA is a new co-operative agent approach, which is derived from the theoretical biology on the topic of ant trail formation and foraging methods. In the ACSA, a set of co-operating agents called "ants" co-operates to find good solution to unit commitment problem of thermal units. The merits of ACSA are parallel search and optimization capabilities. In this study, the problem is decomposed in two subproblems. The unit commitment sub-problem is solved by the ant colony search algorithm method and the economic dispatch sub-problem is solved by the lambda-iteration method. The total production cost of the proposed method is cheaper than those of Dynamic Programming (DP), Lagrange Relaxation (LR) and Genetic Algorithm (GA) on the ten generating units system.
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