A Novel Hybrid Differential Evolution-based Hiking Optimization Algorithm.
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| Title: | A Novel Hybrid Differential Evolution-based Hiking Optimization Algorithm. |
|---|---|
| Authors: | Sun, Kaizhen1 sunkaizh12@163.com, Zhang, Yong1 zy9091@163.com |
| Source: | Engineering Letters. May2026, Vol. 34 Issue 5, p1757-1771. 15p. |
| Subjects: | Differential evolution, Optimization algorithms, Metaheuristic algorithms, Algorithms, Mathematical optimization |
| Abstract: | The Hiking optimization algorithm (HOA) is an algorithm inspired by hiking. Aiming at the shortcomings of the original hiking optimization algorithm, such as easy to fall into local optimum and limited convergence accuracy of complex optimization problems, based on the performance enhancement of the hiking optimization algorithm, a hybrid differential evolution hiking optimization algorithm is proposed, which is called DEHOA. Firstly, by introducing the differential evolution crossover operator, the hiking optimization algorithm is deeply integrated with the crossover idea of differential evolution. The candidate solution is generated by the differential vector and the dimension crossover is performed, which significantly enhances the population diversity and search ability. Secondly, a dual-strategy parallel search of population information interaction optimization is designed to balance exploration and development. At the same time, the update mechanism of the original algorithm and the new solution generated by crossover are maintained, and the optimal solution is retained by greedy strategy selection. Finally, according to the requirements of different iteration stages, an adaptive boundary processing function is introduced to ensure that all boundary-crossing values are forced to be constrained within the upper and lower bounds, so as to avoid the generation of invalid solutions. The three strategies work together to effectively improve the global exploration ability and local convergence performance of the algorithm. In addition, in order to verify the optimization performance of DEHOA algorithm, DEHOA is compared with four advanced meta-heuristic algorithms on the test function CEC2022. Experiments show that the new algorithm shows better optimization accuracy and efficiency on multiple sets of benchmark test functions, and provides a more competitive solution for complex optimization scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193453932 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Novel Hybrid Differential Evolution-based Hiking Optimization Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Kaizhen%22">Sun, Kaizhen</searchLink><relatesTo>1</relatesTo><i> sunkaizh12@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yong%22">Zhang, Yong</searchLink><relatesTo>1</relatesTo><i> zy9091@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Engineering+Letters%22">Engineering Letters</searchLink>. May2026, Vol. 34 Issue 5, p1757-1771. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Differential+evolution%22">Differential evolution</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The Hiking optimization algorithm (HOA) is an algorithm inspired by hiking. Aiming at the shortcomings of the original hiking optimization algorithm, such as easy to fall into local optimum and limited convergence accuracy of complex optimization problems, based on the performance enhancement of the hiking optimization algorithm, a hybrid differential evolution hiking optimization algorithm is proposed, which is called DEHOA. Firstly, by introducing the differential evolution crossover operator, the hiking optimization algorithm is deeply integrated with the crossover idea of differential evolution. The candidate solution is generated by the differential vector and the dimension crossover is performed, which significantly enhances the population diversity and search ability. Secondly, a dual-strategy parallel search of population information interaction optimization is designed to balance exploration and development. At the same time, the update mechanism of the original algorithm and the new solution generated by crossover are maintained, and the optimal solution is retained by greedy strategy selection. Finally, according to the requirements of different iteration stages, an adaptive boundary processing function is introduced to ensure that all boundary-crossing values are forced to be constrained within the upper and lower bounds, so as to avoid the generation of invalid solutions. The three strategies work together to effectively improve the global exploration ability and local convergence performance of the algorithm. In addition, in order to verify the optimization performance of DEHOA algorithm, DEHOA is compared with four advanced meta-heuristic algorithms on the test function CEC2022. Experiments show that the new algorithm shows better optimization accuracy and efficiency on multiple sets of benchmark test functions, and provides a more competitive solution for complex optimization scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Engineering Letters is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1757 Subjects: – SubjectFull: Differential evolution Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: A Novel Hybrid Differential Evolution-based Hiking Optimization Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Kaizhen – PersonEntity: Name: NameFull: Zhang, Yong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1816093X Numbering: – Type: volume Value: 34 – Type: issue Value: 5 Titles: – TitleFull: Engineering Letters Type: main |
| ResultId | 1 |
