Automated Design of Collaboration-Based Hybrid Metaheuristics

Hybridization plays a prominent role in bolstering the performance of optimization algorithms (OAs), yet designing efficient hybrid OAs tailored to intricate optimization problems persists as a formidable task. This article introduces a novel top-down methodology for the automated design of hybrid O...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 54; no. 12; pp. 7877 - 7890
Main Authors: Wang, Yipeng, Xin, Bin, Liu, Bo, Wang, Qing
Format: Journal Article
Language:English
Published: United States IEEE 01.12.2024
Subjects:
Automated algorithm design (AAD)
Automation
Benchmark testing
Design methodology
hybrid metaheuristics (MHs)
Mathematical models
meta-optimization
Metaheuristics
multifactorial optimization
Multitasking
Taxonomy
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Hybridization plays a prominent role in bolstering the performance of optimization algorithms (OAs), yet designing efficient hybrid OAs tailored to intricate optimization problems persists as a formidable task. This article introduces a novel top-down methodology for the automated design of hybrid OAs, treating algorithm design as a meta-optimization problem. A general design template for collaboration-based hybrid OAs is developed, integrating a multitude of hybridization strategies for the first time. Besides, a mathematical model is built to formulate the meta-optimization problem of algorithm design. To address the meta-optimization challenge, an improved multifactorial evolutionary algorithm is proposed to automatically design efficient hybrid metaheuristics in a multitasking environment for the given instances with diverse features. To verify the effectiveness of the proposed design methodology, it is applied to the CEC2017 benchmark functions and the binary knapsack problem. Numerical results have demonstrated the feasibility and effectiveness of the proposed methodology for both continuous and combinatorial optimization benchmarks.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2024.3412997