Meta-heuristic framework: Quantum inspired binary grey wolf optimizer for unit commitment problem. (August 2018)

• Record Type: Journal Article
• Authors: Srikanth, K; Panwar, Lokesh Kumar; Panigrahi, BK; Herrera-Viedma, Enrique; Sangaiah, Arun Kumar; Wang, Gai-Ge
• Is Part Of: Computers & electrical engineering. Volume 70(2018)
• Page Start: 243
• Page End: 260
• Publication Date: 2018-08
• Subjects: Computational intelligence -- Grey wolf optimizer (GWO) -- Unit commitment (UC) -- Quantum computing -- Power system optimization -- Constrained optimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.07.023
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This paper proposes a quantum inspired binary grey wolf optimizer (QI-BGWO) to solve unit commitment (UC) problem. The QI-BGWO integrates quantum computing concepts with BGWO to improve the hunting process of the wolf pack. The inherent properties of Q-bit and Q-gate concepts in quantum computing help in achieving better balance between exploration and exploitation properties of the search process. The position update processes of wolves at different hierarchy levels are replaced by Q-bit's individual probabilistic representation along with dynamic rotation angle and coordinate rotation gate. Therefore, solution approaches exploit the search properties of GWO and quantum computing using quantum bits, gates, superposition principle etc., to solve the unit commitment schedule efficiently. The results and statistical analysis demonstrates the effectiveness of proposed approaches in solving the UC problem and establishes the significance of proposed approaches among existing binary and quantum computing based heuristic approaches.