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Adaptive Bayesian Optimization Algorithm for Unpredictable Business Environments

Published: 03 August 2024 Publication History

Abstract

This paper introduces an adaptive Bayesian optimization (BayesOpt) framework with dynamic conditioning and jitter mechanisms. The new framework enhances the adaptability and effectiveness of optimization in unpredictable business environments. The dynamic scaling in this framework dynamically modifies the mean objective function in each iteration, and adaptive conditioning functions. The adaptive acquisition jitter function enhances adaptability by adjusting the jitter of the acquisition function. The framework is tested using single-objective, multi-objective, and decoupled multi-objective functions. Statistical analyses which include t-statistics, p-values, and effect size measures (Cohen's d and Hedges g) reveal the superiority of the proposed framework over the original Bayes optimization. The primary contribution is developing a novel and effective optimization approach in stochastic environments, especially in the context of supply chain inventory management.

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Attached presentation, "Adaptive Bayesian Optimization Algorithm for Unpredictable Business Environments," emphasizes the importance of optimization in managing uncertainties in modern business contexts. Traditional methods often fail to adapt to dynamic conditions, leading to suboptimal decisions and increased costs. This study introduces the Bayesian Optimization (BayesOpt) framework, which uses probabilistic models to find optimal solutions efficiently. The presentation presents convergence plots and ROI plots to illustrate the effectiveness of the optimization process, compares adaptive Bayesian optimization with decoupled Bayesian optimization, and provides statistical analysis to evaluate the stability and effectiveness of the optimization process over multiple runs. The study suggests future work involving common test functions like Rosenbrock and Rastrigin.

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Cited By

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  • (2025)XAI-BO: an architecture using Grad-CAM technique to evaluate Bayesian optimization algorithms on deep learning modelsJournal of Information and Telecommunication10.1080/24751839.2024.2447191(1-22)Online publication date: 7-Jan-2025

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    ISMSI '24: Proceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
    April 2024
    125 pages
    ISBN:9798400717291
    DOI:10.1145/3665065
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 03 August 2024

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    Author Tags

    1. Adaptive conditioning
    2. Dynamic scaling
    3. Effect size
    4. Optimization algorithm
    5. Probabilistic modeling
    6. Stochastic environments

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    • (2025)XAI-BO: an architecture using Grad-CAM technique to evaluate Bayesian optimization algorithms on deep learning modelsJournal of Information and Telecommunication10.1080/24751839.2024.2447191(1-22)Online publication date: 7-Jan-2025

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