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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ftdoajarticles:oai:doaj.org/article:8108e0c3219b44d89e65bb7e93bda03d 2023-05-15T17:43:04+02:00 An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems Yan Liang Xianzhi Hu Gang Hu Wanting Dou 2022-11-01T00:00:00Z https://doi.org/10.3390/math10224383 https://doaj.org/article/8108e0c3219b44d89e65bb7e93bda03d EN eng MDPI AG https://www.mdpi.com/2227-7390/10/22/4383 https://doaj.org/toc/2227-7390 doi:10.3390/math10224383 2227-7390 https://doaj.org/article/8108e0c3219b44d89e65bb7e93bda03d Mathematics, Vol 10, Iss 4383, p 4383 (2022) northern goshawk optimization algorithm polynomial interpolation opposite learning method engineering optimization problem traveling salesman problem Mathematics QA1-939 article 2022 ftdoajarticles https://doi.org/10.3390/math10224383 2022-12-30T22:37:16Z 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. Article in Journal/Newspaper Northern Goshawk Directory of Open Access Journals: DOAJ Articles Mathematics 10 22 4383 |
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English |
topic |
northern goshawk optimization algorithm polynomial interpolation opposite learning method engineering optimization problem traveling salesman problem Mathematics QA1-939 |
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northern goshawk optimization algorithm polynomial interpolation opposite learning method engineering optimization problem traveling salesman problem Mathematics QA1-939 Yan Liang Xianzhi Hu Gang Hu Wanting Dou An Enhanced Northern Goshawk Optimization Algorithm and Its Application in Practical Optimization Problems |
topic_facet |
northern goshawk optimization algorithm polynomial interpolation opposite learning method engineering optimization problem traveling salesman problem Mathematics QA1-939 |
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. |
format |
Article in Journal/Newspaper |
author |
Yan Liang Xianzhi Hu Gang Hu Wanting Dou |
author_facet |
Yan Liang Xianzhi Hu Gang Hu Wanting Dou |
author_sort |
Yan Liang |
title |
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_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_sort |
enhanced northern goshawk optimization algorithm and its application in practical optimization problems |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/math10224383 https://doaj.org/article/8108e0c3219b44d89e65bb7e93bda03d |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
Mathematics, Vol 10, Iss 4383, p 4383 (2022) |
op_relation |
https://www.mdpi.com/2227-7390/10/22/4383 https://doaj.org/toc/2227-7390 doi:10.3390/math10224383 2227-7390 https://doaj.org/article/8108e0c3219b44d89e65bb7e93bda03d |
op_doi |
https://doi.org/10.3390/math10224383 |
container_title |
Mathematics |
container_volume |
10 |
container_issue |
22 |
container_start_page |
4383 |
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1766145083607875584 |