Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules
This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus sugg...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8706930/ |
| _version_ | 1818323403212324864 |
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| author | Yiqiao Cai Chi Shao Ying Zhou Shunkai Fu Huizhen Zhang Hui Tian |
| author_facet | Yiqiao Cai Chi Shao Ying Zhou Shunkai Fu Huizhen Zhang Hui Tian |
| author_sort | Yiqiao Cai |
| collection | DOAJ |
| description | This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM. |
| first_indexed | 2024-12-13T11:12:08Z |
| format | Article |
| id | doaj.art-70295bc066bc435da2f2cbb893f3966c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T11:12:08Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-70295bc066bc435da2f2cbb893f3966c2022-12-21T23:48:43ZengIEEEIEEE Access2169-35362019-01-017580235804010.1109/ACCESS.2019.29149638706930Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic RulesYiqiao Cai0https://orcid.org/0000-0003-4295-5633Chi Shao1Ying Zhou2Shunkai Fu3Huizhen Zhang4Hui Tian5https://orcid.org/0000-0002-1591-656XCollege of Computer Science and Technology, Huaqiao University, Xiamen, ChinaCollege of Computer Science and Technology, Huaqiao University, Xiamen, ChinaSchool of Computer Sciences, Shenzhen Institute of Information Technology, Shenzhen, ChinaCollege of Computer Science and Technology, Huaqiao University, Xiamen, ChinaCollege of Computer Science and Technology, Huaqiao University, Xiamen, ChinaCollege of Computer Science and Technology, Huaqiao University, Xiamen, ChinaThis paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.https://ieeexplore.ieee.org/document/8706930/Differential evolutionadaptive guiding mechanismheuristic rulemutation operatornumerical optimization |
| spellingShingle | Yiqiao Cai Chi Shao Ying Zhou Shunkai Fu Huizhen Zhang Hui Tian Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules IEEE Access Differential evolution adaptive guiding mechanism heuristic rule mutation operator numerical optimization |
| title | Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules |
| title_full | Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules |
| title_fullStr | Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules |
| title_full_unstemmed | Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules |
| title_short | Differential Evolution With Adaptive Guiding Mechanism Based on Heuristic Rules |
| title_sort | differential evolution with adaptive guiding mechanism based on heuristic rules |
| topic | Differential evolution adaptive guiding mechanism heuristic rule mutation operator numerical optimization |
| url | https://ieeexplore.ieee.org/document/8706930/ |
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