A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization
The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorit...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/8/2916 |
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| author | Xinchao Zhao Rui Li Junling Hao Zhaohua Liu Jianmei Yuan |
| author_facet | Xinchao Zhao Rui Li Junling Hao Zhaohua Liu Jianmei Yuan |
| author_sort | Xinchao Zhao |
| collection | DOAJ |
| description | The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance. |
| first_indexed | 2024-03-10T20:17:02Z |
| format | Article |
| id | doaj.art-0d2271e37822494faea0339e07217516 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:17:02Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0d2271e37822494faea0339e072175162023-11-19T22:27:34ZengMDPI AGApplied Sciences2076-34172020-04-01108291610.3390/app10082916A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global OptimizationXinchao Zhao0Rui Li1Junling Hao2Zhaohua Liu3Jianmei Yuan4School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaSchool of Statistics, University of International Business and Economics, Beijing 10029, ChinaSchool of Science, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaHunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan 411105, ChinaThe canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.https://www.mdpi.com/2076-3417/10/8/2916harmony searchdifferential mutationpopulation size reductionperiodic learning |
| spellingShingle | Xinchao Zhao Rui Li Junling Hao Zhaohua Liu Jianmei Yuan A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization Applied Sciences harmony search differential mutation population size reduction periodic learning |
| title | A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization |
| title_full | A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization |
| title_fullStr | A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization |
| title_full_unstemmed | A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization |
| title_short | A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization |
| title_sort | new differential mutation based adaptive harmony search algorithm for global optimization |
| topic | harmony search differential mutation population size reduction periodic learning |
| url | https://www.mdpi.com/2076-3417/10/8/2916 |
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