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Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems

Author

Listed:
  • Salil Madhav Dubey

    (Department of Electrical Engineering, Madhav Institute of Technology & Science (MITS), Gwalior 474005, India)

  • Hari Mohan Dubey

    (Department of Electrical Engineering, Birsa Institute of Technology Sindri (BIT Sindri), Sindri, Dhanbad 828123, India)

  • Surender Reddy Salkuti

    (Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Korea)

Abstract

This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems.

Suggested Citation

  • Salil Madhav Dubey & Hari Mohan Dubey & Surender Reddy Salkuti, 2022. "Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems," Energies, MDPI, vol. 15(15), pages 1-29, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5704-:d:881452
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    References listed on IDEAS

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    1. Sen Zhang & Yongquan Zhou, 2015. "Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis," Discrete Dynamics in Nature and Society, Hindawi, vol. 2015, pages 1-17, November.
    2. Jiangtao Yu & Chang-Hwan Kim & Abdul Wadood & Tahir Khurshiad & Sang-Bong Rhee, 2018. "A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems," Energies, MDPI, vol. 11(8), pages 1-25, July.
    3. Sarat Chandra Nayak & Bijan Bihari Misra, 2019. "A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-34, December.
    4. Kutaiba Sabah Nimma & Monaaf D. A. Al-Falahi & Hung Duc Nguyen & S. D. G. Jayasinghe & Thair S. Mahmoud & Michael Negnevitsky, 2018. "Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids," Energies, MDPI, vol. 11(4), pages 1-27, April.
    5. B Suman & P Kumar, 2006. "A survey of simulated annealing as a tool for single and multiobjective optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(10), pages 1143-1160, October.
    6. Sultana, U. & Khairuddin, Azhar B. & Mokhtar, A.S. & Zareen, N. & Sultana, Beenish, 2016. "Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system," Energy, Elsevier, vol. 111(C), pages 525-536.
    7. Jayabarathi, T. & Raghunathan, T. & Adarsh, B.R. & Suganthan, Ponnuthurai Nagaratnam, 2016. "Economic dispatch using hybrid grey wolf optimizer," Energy, Elsevier, vol. 111(C), pages 630-641.
    8. Shu-Xia Li & Jie-Sheng Wang, 2015. "Dynamic Modeling of Steam Condenser and Design of PI Controller Based on Grey Wolf Optimizer," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-9, December.
    9. Mahamed G. H. Omran & Maurice Clerc & Fatme Ghaddar & Ahmad Aldabagh & Omar Tawfik, 2022. "Permutation Tests for Metaheuristic Algorithms," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    10. Basu, M. & Chowdhury, A., 2013. "Cuckoo search algorithm for economic dispatch," Energy, Elsevier, vol. 60(C), pages 99-108.
    11. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
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    Cited by:

    1. Sushmita Kujur & Hari Mohan Dubey & Surender Reddy Salkuti, 2023. "Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    2. Aokang Pang & Huijun Liang & Chenhao Lin & Lei Yao, 2023. "A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch," Energies, MDPI, vol. 16(2), pages 1-23, January.

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