Deep learning at the service of metaheuristics for solving numerical optimization problems

Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learn...

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Bibliographic Details
Published in:Neural computing & applications Vol. 37; no. 27; pp. 22493 - 22528
Main Authors: Oyelade, Olaide N., Ezugwu, Absalom E., Saha, Apu K., Thieu, Nguyen V., Gandomi, Amir H.
Format: Journal Article
Language:English
Published: London Springer London 01.09.2025
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Image Processing and Computer Vision
Probability and Statistics in Computer Science
S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications
Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications
Benchmark functions
Deep learning
Long short-term memory, LSTM
Neural networks
Metaheuristic algorithms
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:Integrating deep learning methods into metaheuristic algorithms has gained attention for addressing design-related issues and enhancing performance. The primary objective is to improve solution quality and convergence speed within solution search spaces. This study investigates the use of deep learning methods as a generative model to learn historical content, including global best and worst solutions, solution sequences, function evaluation patterns, solution space characteristics, population modification trajectories, and movement between local and global search processes. An LSTM-based architecture is trained on dynamic optimization data collected during the metaheuristic optimization process. The trained model generates an initial solution space and is integrated into the optimization algorithms to intelligently monitor the search process during exploration and exploitation phases. The proposed deep learning-based methods are evaluated on 55 benchmark functions of varying complexities, including CEC 2017 and compared with 13 biology-based, evolution-based, and swarm-based metaheuristic algorithms. Experimental results demonstrate that all the deep learning-based optimization algorithms achieve high-quality solutions, faster convergence rates, and significant performance improvements. These findings highlight the critical role of deep learning in addressing design issues, enhancing solution quality, trajectory, and performance speed in metaheuristic algorithms.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-024-10610-7