Multiscale medalist learning algorithm and its application in engineering

This paper presents the Multiscale Medalist Learning Algorithm (MMLA) as a novel heuristic approach for complex engineering optimization problems. By extending the Medalist Learning Algorithm, MMLA offers enhanced solution efficiency. The algorithm divides the learning process into successive period...

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Bibliographic Details
Published in:Acta mechanica 2024-02, Vol.235 (2), p.751-777
Main Authors: He, Sheng-Xue, Cui, Yun-Ting
Format: Article
Language:English
Subjects:
Algorithms
Behavior
Classical and Continuum Physics
Control
Design engineering
Design optimization
Dynamical Systems
Efficiency
Engineering
Engineering Fluid Dynamics
Engineering Thermodynamics
Genetic algorithms
Heat and Mass Transfer
Heuristic
Heuristic methods
Learning
Machine learning
Neighborhoods
Optimization algorithms
Original Paper
Solid Mechanics
Students
Theoretical and Applied Mechanics
Vibration
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Summary:This paper presents the Multiscale Medalist Learning Algorithm (MMLA) as a novel heuristic approach for complex engineering optimization problems. By extending the Medalist Learning Algorithm, MMLA offers enhanced solution efficiency. The algorithm divides the learning process into successive periods with reduced search spaces, enabling focused search efforts in promising areas. Within each period, predefined learning stages are implemented. Top performers, known as medalists, engage in self-improvement operations through neighborhood searches, while common learners either learn from medalists or adapt based on the current state using neighborhood fluctuation. The MMLA balances exploration and exploitation capabilities through a natural growth curve that determines the learning efficiency. The MMLA's effectiveness and robustness are illustrated through the solution of a two-dimensional benchmark optimization problem and the successful resolution of ten well-known engineering design optimization problems. Comparative analysis demonstrates that the MMLA consistently outperforms other algorithms, providing competitive solutions with strict feasibility and minimal variation.
ISSN:0001-5970
1619-6937
DOI:10.1007/s00707-023-03773-2