An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems
As a kind of effective tool in solving complex optimization problems, intelligent optimization algorithms are paid more attention to their advantages of being easy to implement and their wide applicability. This paper proposes an enhanced northern goshawk optimization algorithm to further improve th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/22/4383 |
| _version_ | 1797464628739964928 |
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| author | Yan Liang Xianzhi Hu Gang Hu Wanting Dou |
| author_facet | Yan Liang Xianzhi Hu Gang Hu Wanting Dou |
| author_sort | Yan Liang |
| collection | DOAJ |
| description | As a kind of effective tool in solving complex optimization problems, intelligent optimization algorithms are paid more attention to their advantages of being easy to implement and their wide applicability. This paper proposes an enhanced northern goshawk optimization algorithm to further improve the ability to solve challenging tasks. Firstly, by applying the polynomial interpolation strategy to the whole population, the quality of the solutions can be enhanced to keep a fast convergence to the better individual. Then, to avoid falling into lots of local optimums, especially late in the whole search, different kinds of opposite learning methods are used to help the algorithm to search the space more fully, including opposite learning, quasi-opposite learning, and quasi-reflected learning, to keep the diversity of the population, which is noted as a multi-strategy opposite learning method in this paper. Following the construction of the enhanced algorithm, its performance is analyzed by solving the CEC2017 test suite, and five practical optimization problems. Results show that the enhanced algorithm ranks first on 23 test functions, accounting for 79.31% among 29 functions, and keeps a faster convergence speed and a better stability on most functions, compared with the original northern goshawk optimization algorithm and other popular algorithms. For practical problems, the enhanced algorithm is still effective. When the complexity of the TSP is increased, the performance of the improved algorithm is much better than others on all measure indexes. Thus, the enhanced algorithm can keep the balance between exploitation and exploration and obtain better solutions with a faster speed for problems of high complexity. |
| first_indexed | 2024-03-09T18:09:59Z |
| format | Article |
| id | doaj.art-8108e0c3219b44d89e65bb7e93bda03d |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T18:09:59Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8108e0c3219b44d89e65bb7e93bda03d2023-11-24T09:10:43ZengMDPI AGMathematics2227-73902022-11-011022438310.3390/math10224383An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization ProblemsYan Liang0Xianzhi Hu1Gang Hu2Wanting Dou3School of Technology, Xi’an Siyuan University, Xi’an 710038, ChinaDivision of Informationize Management, Xi’an University of Technology, Xi’an 710048, ChinaDepartment of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, ChinaSchool of Technology, Xi’an Siyuan University, Xi’an 710038, ChinaAs a kind of effective tool in solving complex optimization problems, intelligent optimization algorithms are paid more attention to their advantages of being easy to implement and their wide applicability. This paper proposes an enhanced northern goshawk optimization algorithm to further improve the ability to solve challenging tasks. Firstly, by applying the polynomial interpolation strategy to the whole population, the quality of the solutions can be enhanced to keep a fast convergence to the better individual. Then, to avoid falling into lots of local optimums, especially late in the whole search, different kinds of opposite learning methods are used to help the algorithm to search the space more fully, including opposite learning, quasi-opposite learning, and quasi-reflected learning, to keep the diversity of the population, which is noted as a multi-strategy opposite learning method in this paper. Following the construction of the enhanced algorithm, its performance is analyzed by solving the CEC2017 test suite, and five practical optimization problems. Results show that the enhanced algorithm ranks first on 23 test functions, accounting for 79.31% among 29 functions, and keeps a faster convergence speed and a better stability on most functions, compared with the original northern goshawk optimization algorithm and other popular algorithms. For practical problems, the enhanced algorithm is still effective. When the complexity of the TSP is increased, the performance of the improved algorithm is much better than others on all measure indexes. Thus, the enhanced algorithm can keep the balance between exploitation and exploration and obtain better solutions with a faster speed for problems of high complexity.https://www.mdpi.com/2227-7390/10/22/4383northern goshawk optimization algorithmpolynomial interpolationopposite learning methodengineering optimization problemtraveling salesman problem |
| spellingShingle | Yan Liang Xianzhi Hu Gang Hu Wanting Dou An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems Mathematics northern goshawk optimization algorithm polynomial interpolation opposite learning method engineering optimization problem traveling salesman problem |
| title | An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems |
| title_full | An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems |
| title_fullStr | An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems |
| title_full_unstemmed | An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems |
| title_short | An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems |
| title_sort | enhanced northern goshawk optimization algorithm and its application in practical optimization problems |
| topic | northern goshawk optimization algorithm polynomial interpolation opposite learning method engineering optimization problem traveling salesman problem |
| url | https://www.mdpi.com/2227-7390/10/22/4383 |
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