T h e paper presents a n A n t Colony Search Algorithm (ACSA)-based approach t o solve t h e unit commitment (UC) problem. This ACSA algorithm is a relatively new metaheuristic for solving h a r d combinatorial optimization problems. It is a population-based approach t h a t uses exploitation of positive feedback, distributed computation as well as constructive greedy heuristic. Positive feedback is for fast discovery of good solutions, distributed computation avoids early convergence, a n d t h e greedy heuristic helps find adequate solutions in t h e early stages of t h e search process. T h e ACSA was inspired from natural behavior of t h e a n t colonies on how they find t h e food source and bring them back to their nest by building the unique trail formation. T h e U C problem solved using the proposed approach is subject to real power balance, real power operating limits of generating units, spinning reserve, start u p cost, a n d minimum u p a n d down time constraints. T h e proposed approach determines t h e search space of multi-stage scheduling followed by considering the unit transition related constraints d u r i n g t h e process of state transition. T h e paper describes the proposed approach a n d presents test results on a IO-unit test system t h a t demonstrates its effectiveness in solving the U C problem.Index Terms -A n t colony search algorithm, distributed cooperative agents, optimization, unit commitment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.