A Comparative Study of Simulated Annealing and Ant Colony Optimization for Optimizing MRI-Based Alzheimer's Disease Classification
Keywords:
Classification, Metaheuristic optimisation, Simulated Annealing, Ant Colony OptimizationAbstract
The prevalence and devastation of Alzheimer's disease (AD), a neurodegenerative condition, pose a growing threat to world health. The optimisation of numerous factors, including as feature selection, hyperparameters, and model architecture, is necessary for these models to be effective. The performance and accuracy of AD classification models can be improved by using metaheuristic optimisation methods like Simulated Annealing (SA) and Ant Colony Optimization (ACO).In this study, the effectiveness of SA and ACO in improving MRI-based AD classification models is thoroughly compared. ACO and SA both offer distinctive techniques to optimisation, drawing inspiration from ant foraging behaviour and the annealing process in metallurgy, respectively. The paper includes a thorough analysis of the body of work on machine learning algorithm and optimisation methods for AD classification. In the context of model optimisation, it also offers insights into the foundational ideas and practical uses of SA and ACO. We seek to compare the performance of different optimisation techniques in terms of classification accuracy, resilience, and computational economy through careful experimentation and analysis.The findings of this comparison study may help researchers and medical professionals decide which optimisation strategy will improve the precision and dependability of MRI-based AD classification. This study contributes to the ongoing efforts to improve early AD diagnosis by extending our knowledge of how SA and ACO might be used in this crucial area, ultimately improving patient treatment and outcomes.
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