Performance comparison of six algorithms—MOPSO, rNSGA-II, IM-MOEA/D, MOEA/D-FRR-MAB, dMOPSO, and QLNSGA-II—on selected UF test functions: UF1, UF4, UF7, UF8, and UF9.

Performance comparison of six algorithms—MOPSO, rNSGA-II, IM-MOEA/D, MOEA/D-FRR-MAB, dMOPSO, and QLNSGA-II—on selected UF test functions: UF1, UF4, UF7, UF8, and UF9.

<p>Performance comparison of six algorithms—MOPSO, rNSGA-II, IM-MOEA/D, MOEA/D-FRR-MAB, dMOPSO, and QLNSGA-II—on selected UF test functions: UF1, UF4, UF7, UF8, and UF9.</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Zhongfeng Li (3965060) (author)
مؤلفون آخرون: Lei Liu (5074) (author), Zhenlong Zhao (338446) (author), Shujie Mu (22190712) (author), Dong Li (212687) (author), Yuting Zhuo (10147412) (author)
منشور في: 2025
الموضوعات:
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
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500 tonnes
150 tonnes
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الوصف
الملخص:<p>Performance comparison of six algorithms—MOPSO, rNSGA-II, IM-MOEA/D, MOEA/D-FRR-MAB, dMOPSO, and QLNSGA-II—on selected UF test functions: UF1, UF4, UF7, UF8, and UF9.</p>

مواد مشابهة