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Quantum-Inspired Computational Intelligence for Economic Emission Dispatch Problem
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Author(s): Fahad Parvez Mahdi (Universiti Teknologi Petronas, Malaysia), Pandian Vasant (University of Technology Petronas, Malaysia), Vish Kallimani (Universiti Teknologi Petronas, Malaysia), M. Abdullah-Al-Wadud (King Saud University, Saudi Arabia)and Junzo Watada (Universiti Teknologi PETRONAS, Malaysia)
Copyright: 2017
Pages: 24
Source title:
Handbook of Research on Soft Computing and Nature-Inspired Algorithms
Source Author(s)/Editor(s): Shishir K. Shandilya (Bansal Institute of Research and Technology, India), Smita Shandilya (Sagar Institute of Research Technology and Science, India), Kusum Deep (Indian Institute of Technology Roorkee, India)and Atulya K. Nagar (Liverpool Hope University, UK)
DOI: 10.4018/978-1-5225-2128-0.ch015
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Abstract
Economic emission dispatch (EED) problems are one of the most crucial problems in power systems. Growing energy demand, limited reserves of fossil fuel and global warming make this topic into the center of discussion and research. In this chapter, we will discuss the use and scope of different quantum inspired computational intelligence (QCI) methods for solving EED problems. We will evaluate each previously used QCI methods for EED problem and discuss their superiority and credibility against other methods. We will also discuss the potentiality of using other quantum inspired CI methods like quantum bat algorithm (QBA), quantum cuckoo search (QCS), and quantum teaching and learning based optimization (QTLBO) technique for further development in this area.
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