NOVEL ENRICHED TEACHING - LEARNING INSPIRED OPTIMISATION AND QUANTUM OPPONENT PROCESS THEORY-BASED ALGORITHM FOR POWER LOSS DIMINUTION
DOI:
https://doi.org/10.55766/sujst8159Keywords:
Teaching, Learning, Teacher, StudentsAbstract
The enriched teaching-learning-inspired optimization algorithm (ETLO) and quantum opponent process theory-based (QOPT) algorithm are applied to solve the problem of reducing power loss. In this procedure, each learner in the population acts as a searcher and serves as a potential solution. During the course phases and statistics delivery, learners are trained to improve their skills in order to provide better solutions. The teacher will explore various methods to solve the problem as soon as possible. The environment of the human mind is ambiguous. In the exploitation period, learners of the procedure populations examine locally to discover improved solutions. The QOPT algorithm is based on how individuals observe changes in color and sensation over time. The opponent process theory explains how an individual experiences different color images in the time period following their exposure to the original image's color. The algorithm observes an individual's color over a specific time period as the original image appears and disappears. In the retina of the eye, the photochemical effect will be there for a certain period. The size of the object signifies the present rate of the candidate solution, and the distance of the object characterizes the extent from the finest solution. Enriched teaching - learning inspired optimization algorithm (ETLO) and Quantum Opponent process theory based (QOPT) algorithm validated in 7 benchmark functions and IEEE 118, 300 bus systems.
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