Neighborhood operators (actions) in Q-learning.

<div><p>Coal blending in thermal power plants is a complex multi-objective challenge involving economic, operational and environmental considerations. This study presents a Q-learning-enhanced NSGA-II (QLNSGA-II) algorithm that integrates the adaptive policy optimization of Q-learning wi...

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Bibliographic Details
Main Author:	Zhongfeng Li (3965060) (author)
Other Authors:	Lei Liu (5074) (author), Zhenlong Zhao (338446) (author), Shujie Mu (22190712) (author), Dong Li (212687) (author), Yuting Zhuo (10147412) (author)
Published:	2025
Subjects:	Ecology Mental Health Biological Sciences not elsewhere classified walking fish group time performance metrics thermal power plants net heat rate mutation rates based inverted generational distance dynamically adjust crossover conventional blending methods adaptive policy optimization sustainable coal blending results highlight qlnsga objective coal blending div >< p objective optimization coal blending sub >< x </ unconstrained function sulphur content suites show study presents slagging incidence robust solution promising way prevailing algorithms pollutant emission plant ’ industrial validation fuel cost environmental considerations ensuring compliance elitist selection benefits include benchmark tests ash fusion annual savings ></ sub 500 tonnes 150 tonnes
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Neighborhood operators (actions) in Q-learning.

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