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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| Published in: | IEEE transactions on cybernetics Vol. 54; no. 12; pp. 7877 - 7890 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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United States
IEEE
01.12.2024
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| ISSN: | 2168-2267, 2168-2275, 2168-2275 |
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| Abstract | 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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| AbstractList | 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.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. 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. |
| Author | Xin, Bin Wang, Yipeng Liu, Bo Wang, Qing |
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| SubjectTerms | Automated algorithm design (AAD) Automation Benchmark testing Design methodology hybrid metaheuristics (MHs) Mathematical models meta-optimization Metaheuristics multifactorial optimization Multitasking Taxonomy |
| Title | Automated Design of Collaboration-Based Hybrid Metaheuristics |
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