Machine Learning for Bone Cancer Diagnosis: Evaluating Predictive Model Efficacy
DOI:
https://doi.org/10.64252/nqvkpd16Keywords:
Cogeneration, Artificial Bee Colony Optimization, Simulated Annealing, Adaptive Penalty function, Security constraints.Abstract
An intricate non-linear and non-convex optimisation problem is the Combined Heat and Economic Dispatch (CHPED) problem. This study offers an Artificial Bee Colony (ABC) method embedded with Simulated Annealing (SA) and Adaptive Penalty (AP) function, which together solve the CHPED problem. This algorithm is referred to as ABC-SA-AP. On both modified and current CHPED test systems, the ABC-SA-AP algorithm is used. The outcome shows how effective ABC-SA-AP is at handling the CHPED problem.
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Published
2025-05-10
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How to Cite
Machine Learning for Bone Cancer Diagnosis: Evaluating Predictive Model Efficacy. (2025). International Journal of Environmental Sciences, 11(4s), 1332-1340. https://doi.org/10.64252/nqvkpd16