A Scalable Deep Learning Multi-Criteria Decision-Making Framework for Cloud Service Provider Selection by Multilayer Perceptron Optimized by Honey Badger Algorithm

Abhaprakash Praharaj; Jyotiranjan Hota

doi:10.57159/jcmm.5.3.26857

Authors

Abhaprakash Praharaj KIIT School of Management, KIIT Deemed to be University, Bhubaneswar, Odisha, India https://orcid.org/0009-0000-8610-1539
Jyotiranjan Hota KIIT School of Management, KIIT Deemed to be University, Bhubaneswar, Odisha, India https://orcid.org/0000-0002-9483-2689

DOI:

https://doi.org/10.57159/jcmm.5.3.26857

Keywords:

Multilayer Perceptron, Honey Badger Algorithm, Hyperparameter Optimization, Multi-Criteria Decision Making, Cloud Service Selection

Abstract

Selecting cloud services requires balancing multiple criteria such as cost, security, performance, and compliance, which makes it a complex multi-criteria decision-making (MCDM) problem. Traditional MCDM techniques such as the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) often fall short in dynamic cloud environments because they rely on static, expert-defined weights and struggle to capture nonlinear interactions among criteria. To address these limitations, this study proposes a deep-learning-based MCDM framework that integrates a Multilayer Perceptron (MLP) with the Honey Badger Algorithm (HBA) for hyperparameter optimization. The MLP models nonlinear dependencies in tabular decision data, while the HBA enhances performance through adaptive metaheuristic search. The results demonstrate superior accuracy, lower error rates, and improved generalization compared with both traditional MCDM methods and state-of-the-art machine-learning baselines. The contributions of this work lie in advancing cloud service selection from provider-level to fine-grained criteria-level evaluation, establishing a reproducible benchmark, and offering a scalable, interpretable decision-making framework for next-generation cloud computing environments.

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