A Gravitational Search Algorithm With Chaotic Neural Oscillators
Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an impo...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8981995/ |
| id |
doaj-2a65b75238ff4c67b31ddaa80fe42768 |
|---|---|
| record_format |
Article |
| spelling |
doaj-2a65b75238ff4c67b31ddaa80fe427682021-03-30T02:23:19ZengIEEEIEEE Access2169-35362020-01-018259382594810.1109/ACCESS.2020.29715058981995A Gravitational Search Algorithm With Chaotic Neural OscillatorsYirui Wang0https://orcid.org/0000-0001-5767-3343Shangce Gao1https://orcid.org/0000-0001-5042-3261Yang Yu2https://orcid.org/0000-0002-4724-9933Ziqian Wang3Jiujun Cheng4https://orcid.org/0000-0001-5176-4762Todo Yuki5Faculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanDepartment of Computer Science and Technology, Ministry of Education, Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, ChinaFaculty of Electrical and Information and Computer Engineering, Kanazawa University, Kanazawa-shi, JapanGravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO.https://ieeexplore.ieee.org/document/8981995/Chaotic neural oscillatorchaotic stategravitational constantgravitational search algorithm |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Yirui Wang Shangce Gao Yang Yu Ziqian Wang Jiujun Cheng Todo Yuki |
| spellingShingle |
Yirui Wang Shangce Gao Yang Yu Ziqian Wang Jiujun Cheng Todo Yuki A Gravitational Search Algorithm With Chaotic Neural Oscillators IEEE Access Chaotic neural oscillator chaotic state gravitational constant gravitational search algorithm |
| author_facet |
Yirui Wang Shangce Gao Yang Yu Ziqian Wang Jiujun Cheng Todo Yuki |
| author_sort |
Yirui Wang |
| title |
A Gravitational Search Algorithm With Chaotic Neural Oscillators |
| title_short |
A Gravitational Search Algorithm With Chaotic Neural Oscillators |
| title_full |
A Gravitational Search Algorithm With Chaotic Neural Oscillators |
| title_fullStr |
A Gravitational Search Algorithm With Chaotic Neural Oscillators |
| title_full_unstemmed |
A Gravitational Search Algorithm With Chaotic Neural Oscillators |
| title_sort |
gravitational search algorithm with chaotic neural oscillators |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2020-01-01 |
| description |
Gravitational search algorithm (GSA) inspired from physics emulates gravitational forces to guide particles' search. It has been successfully applied to diverse optimization problems. However, its search performance is limited by its inherent mechanism where gravitational constant plays an important role in gravitational forces among particles. To improve it, this paper uses chaotic neural oscillators to adjust its gravitational constant, named GSA-CNO. Chaotic neural oscillators can generate various chaotic states according to their parameter settings. Thus, we select four kinds of chaotic neural oscillators to form distinctive chaotic characteristics. Experimental results show that chaotic neural oscillators effectively tune the gravitational constant such that GSA-CNO has good performance and stability against four GSA variants on functions. Three real-world optimization problems demonstrate the promising practicality of GSA-CNO. |
| topic |
Chaotic neural oscillator chaotic state gravitational constant gravitational search algorithm |
| url |
https://ieeexplore.ieee.org/document/8981995/ |
| work_keys_str_mv |
AT yiruiwang agravitationalsearchalgorithmwithchaoticneuraloscillators AT shangcegao agravitationalsearchalgorithmwithchaoticneuraloscillators AT yangyu agravitationalsearchalgorithmwithchaoticneuraloscillators AT ziqianwang agravitationalsearchalgorithmwithchaoticneuraloscillators AT jiujuncheng agravitationalsearchalgorithmwithchaoticneuraloscillators AT todoyuki agravitationalsearchalgorithmwithchaoticneuraloscillators AT yiruiwang gravitationalsearchalgorithmwithchaoticneuraloscillators AT shangcegao gravitationalsearchalgorithmwithchaoticneuraloscillators AT yangyu gravitationalsearchalgorithmwithchaoticneuraloscillators AT ziqianwang gravitationalsearchalgorithmwithchaoticneuraloscillators AT jiujuncheng gravitationalsearchalgorithmwithchaoticneuraloscillators AT todoyuki gravitationalsearchalgorithmwithchaoticneuraloscillators |
| _version_ |
1724185268521533440 |