AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms With Diverse Structures

Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyon...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computational intelligence Vol. 9; no. 5; pp. 3690 - 3703
Main Authors: Zhao, Qi, Yan, Bai, Hu, Taiwei, Chen, Xianglong, Yang, Jian, Cheng, Shi, Shi, Yuhui
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
automated algorithm design
automated machine learning
Automation
Binary trees
Computer science
evolutionary algorithm
Genetic algorithms
Graph representations
Graphical representations
Heuristic algorithms
Heuristic methods
Metaheuristic
Metaheuristics
optimization
Performance enhancement
Pipelines
Problem-solving
Prototypes
Solvers
Space exploration
Vectors
ISSN:2471-285X, 2471-285X
Online Access:Get full text
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Summary:Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.
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ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2025.3561629