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Real power loss reduction by Q-learning and hyper-heuristic method

Author

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  • Lenin Kanagasabai

    (Prasad V. Potluri Siddhartha Institute of Technology)

Abstract

This paper proposes an algorithm endorsement design using Q-learning and hyper-heuristic method (QH) to support choice architects select the supreme appropriate bio-inspired algorithm for the power loss reduction problem. For this an artificial bee colony (ABC) algorithm, Mobulidae optimization algorithm (MOA), enhanced Salp swarm algorithm (ESS) and Orcinus orca optimization (OOO) algorithm are employed as small level optimizers consequently that the Q-learning and hyper-heuristic robotically pick the optimizer in every cycle of the optimization procedure. Q-learning is a prototypical unrestricted fortification learning procedure to discover the optimal solution. In Q-learning, representatives interrelate with the environs, and their segment is rationalized. At every segment, a representative does engagements and obtains an incentive or fine. Q-learning contains of five constituents, including representatives, environs, engagements, segment, and incentive. In this paper, Q-learning intends to pick the bio-inspired algorithm in every series of the run. Hyper-heuristic is demarcated as an elevated heuristic that exploits a set of small level heuristics to determine the preeminent solution. A hyper-heuristic is frequently used to select a local examine tool such as inset, passage, and exchange. In this paper ABC, MOA, ESS, and OOO are engaged as small level heuristics. As a response tool, extra fruitful algorithms are endorsed based on the grade of development. Proposed QH is corroborated in IEEE 30 bus system and loss lessening is amplified.

Suggested Citation

  • Lenin Kanagasabai, 2022. "Real power loss reduction by Q-learning and hyper-heuristic method," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1607-1622, August.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01516-x
    DOI: 10.1007/s13198-021-01516-x
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    References listed on IDEAS

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    1. Subbaraj, P. & Rengaraj, R. & Salivahanan, S., 2009. "Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm," Applied Energy, Elsevier, vol. 86(6), pages 915-921, June.
    2. Edmund K Burke & Michel Gendreau & Matthew Hyde & Graham Kendall & Gabriela Ochoa & Ender Özcan & Rong Qu, 2013. "Hyper-heuristics: a survey of the state of the art," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(12), pages 1695-1724, December.
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