A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications
The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow converge...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/15/6/189 |
| _version_ | 1797490811637596160 |
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| author | Quan Zhang Shiyu Du Yiming Zhang Hongzhuo Wu Kai Duan Yanru Lin |
| author_facet | Quan Zhang Shiyu Du Yiming Zhang Hongzhuo Wu Kai Duan Yanru Lin |
| author_sort | Quan Zhang |
| collection | DOAJ |
| description | The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population’s diversity and accelerates the algorithm’s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance. |
| first_indexed | 2024-03-10T00:38:24Z |
| format | Article |
| id | doaj.art-7df704372c8944ff841f7f3bc240b9a9 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T00:38:24Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-7df704372c8944ff841f7f3bc240b9a92023-11-23T15:13:01ZengMDPI AGAlgorithms1999-48932022-05-0115618910.3390/a15060189A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering ApplicationsQuan Zhang0Shiyu Du1Yiming Zhang2Hongzhuo Wu3Kai Duan4Yanru Lin5Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaEngineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaEngineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, ChinaThe Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population’s diversity and accelerates the algorithm’s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance.https://www.mdpi.com/1999-4893/15/6/189chimp optimization algorithmrefraction learningtent chaosglobal optimizationengineering design optimization |
| spellingShingle | Quan Zhang Shiyu Du Yiming Zhang Hongzhuo Wu Kai Duan Yanru Lin A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications Algorithms chimp optimization algorithm refraction learning tent chaos global optimization engineering design optimization |
| title | A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications |
| title_full | A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications |
| title_fullStr | A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications |
| title_full_unstemmed | A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications |
| title_short | A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications |
| title_sort | novel chimp optimization algorithm with refraction learning and its engineering applications |
| topic | chimp optimization algorithm refraction learning tent chaos global optimization engineering design optimization |
| url | https://www.mdpi.com/1999-4893/15/6/189 |
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