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Enhancing Model Accuracy: A Parameter Optimization Strategy Based on the Dream Optimization Algorithm
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Enhancing Model Accuracy: A Parameter Optimization Strategy Based on the Dream Optimization Algorithm
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Journal Article
Enhancing Model Accuracy: A Parameter Optimization Strategy Based on the Dream Optimization Algorithm
2025
Overview
Reliable parameter identification is essential for the development and predictive use of non-linear bioprocess models. This study evaluates the recently proposed Dream Optimization Algorithm (DOA), a human-inspired metaheuristic based on memory retention, partial forgetting, and dream-sharing mechanisms, for the identification of kinetic parameters in an Escherichia coli fed-batch cultivation model. The algorithm’s performance is assessed using experimental cultivation data and compared with three widely employed metaheuristics: the genetic algorithm (GA), simulated annealing (SA), and the crow search algorithm (CSA). Results demonstrate that DOA achieves the lowest objective function value, the best mean performance across 30 independent runs, and substantially reduced computational time compared to SA and CSA. The model dynamics generated using DOA-identified parameters show excellent agreement with experimental biomass and substrate measurements, even in the presence of significant noise in the substrate data. These findings highlight the high accuracy, robustness, and computational efficiency of DOA, confirming its strong potential as an effective tool for bioprocess model parameter estimation and broader non-linear optimization tasks.
Publisher
Bulgarska Akademiya na Naukite / Bulgarian Academy of Sciences,Bulgarian Academy of Sciences
