Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design
In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the sea...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/9/1/21 |
| _version_ | 1827372653728497664 |
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| author | Jiaxu Huang Haiqing Hu |
| author_facet | Jiaxu Huang Haiqing Hu |
| author_sort | Jiaxu Huang |
| collection | DOAJ |
| description | In this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the search area of the population, enhances global search ability, and improves population diversity. In the honey harvesting stage of the honey badger (development), differential mutation strategies are combined, selectively introducing local quantum search strategies that enhance local search capabilities and improve population optimization accuracy, or introducing dynamic Laplacian crossover operators that can improve convergence speed, while reducing the odds of the HBA sinking into local optima. Through comparative experiments with other algorithms on the CEC2017, CEC2020, and CEC2022 test sets, and three engineering examples, EHBA has been verified to have good solving performance. From the comparative analysis of convergence graphs, box plots, and algorithm performance tests, it can be seen that compared with the other eight algorithms, EHBA has better results, significantly improving its optimization ability and convergence speed, and has good application prospects in the field of optimization problems. |
| first_indexed | 2024-03-08T11:04:08Z |
| format | Article |
| id | doaj.art-32d652fa2cdd478b9abf1fa256a0d2fa |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T11:04:08Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-32d652fa2cdd478b9abf1fa256a0d2fa2024-01-26T15:15:35ZengMDPI AGBiomimetics2313-76732024-01-01912110.3390/biomimetics9010021Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product DesignJiaxu Huang0Haiqing Hu1School of Economics and Management, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Economics and Management, Xi’an University of Technology, Xi’an 710054, ChinaIn this paper, a multi-strategy fusion enhanced Honey Badger algorithm (EHBA) is proposed to address the problem of easy convergence to local optima and difficulty in achieving fast convergence in the Honey Badger algorithm (HBA). The adoption of a dynamic opposite learning strategy broadens the search area of the population, enhances global search ability, and improves population diversity. In the honey harvesting stage of the honey badger (development), differential mutation strategies are combined, selectively introducing local quantum search strategies that enhance local search capabilities and improve population optimization accuracy, or introducing dynamic Laplacian crossover operators that can improve convergence speed, while reducing the odds of the HBA sinking into local optima. Through comparative experiments with other algorithms on the CEC2017, CEC2020, and CEC2022 test sets, and three engineering examples, EHBA has been verified to have good solving performance. From the comparative analysis of convergence graphs, box plots, and algorithm performance tests, it can be seen that compared with the other eight algorithms, EHBA has better results, significantly improving its optimization ability and convergence speed, and has good application prospects in the field of optimization problems.https://www.mdpi.com/2313-7673/9/1/21differential mutation operationdynamic opposite learning strategydynamic Laplace crossoverhoney badger algorithmquantum local search |
| spellingShingle | Jiaxu Huang Haiqing Hu Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design Biomimetics differential mutation operation dynamic opposite learning strategy dynamic Laplace crossover honey badger algorithm quantum local search |
| title | Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design |
| title_full | Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design |
| title_fullStr | Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design |
| title_full_unstemmed | Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design |
| title_short | Differential Mutation Incorporated Quantum Honey Badger Algorithm with Dynamic Opposite Learning and Laplace Crossover for Fuzzy Front-End Product Design |
| title_sort | differential mutation incorporated quantum honey badger algorithm with dynamic opposite learning and laplace crossover for fuzzy front end product design |
| topic | differential mutation operation dynamic opposite learning strategy dynamic Laplace crossover honey badger algorithm quantum local search |
| url | https://www.mdpi.com/2313-7673/9/1/21 |
| work_keys_str_mv | AT jiaxuhuang differentialmutationincorporatedquantumhoneybadgeralgorithmwithdynamicoppositelearningandlaplacecrossoverforfuzzyfrontendproductdesign AT haiqinghu differentialmutationincorporatedquantumhoneybadgeralgorithmwithdynamicoppositelearningandlaplacecrossoverforfuzzyfrontendproductdesign |