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A Novel Binary Ant Colony Optimization: Application to the Unit Commitment Problem of Power Systems

  • Jang, Se-Hwan ;
  • Roh, Jae-Hyung ;
  • Kim, Wook ;
  • Sherpa, Tenzi ;
  • Kim, Jin-Ho ;
  • Park, Jong-Bae
  • Received : 2010.06.11
  • Accepted : 2010.09.01
  • Published : 2011.03.01

Abstract

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

Keywords

Binary ant colony optimization;Combinatorial optimization;Unit commitment;Swarm intelligence

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