Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization
Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8928579/ |
| _version_ | 1798001924649254912 |
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| author | Rashida Adeeb Khanum Muhammad Asif Jan Abdulaziz Aldegheishem Amjad Mehmood Nabil Alrajeh Akbar Khanan |
| author_facet | Rashida Adeeb Khanum Muhammad Asif Jan Abdulaziz Aldegheishem Amjad Mehmood Nabil Alrajeh Akbar Khanan |
| author_sort | Rashida Adeeb Khanum |
| collection | DOAJ |
| description | Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski’s information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided. |
| first_indexed | 2024-04-11T11:45:03Z |
| format | Article |
| id | doaj.art-fff956ea548f4b4b83f359513c58ccb6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:45:03Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fff956ea548f4b4b83f359513c58ccb62022-12-22T04:25:42ZengIEEEIEEE Access2169-35362020-01-018308053082510.1109/ACCESS.2019.29582888928579Two New Improved Variants of Grey Wolf Optimizer for Unconstrained OptimizationRashida Adeeb Khanum0https://orcid.org/0000-0002-5255-5580Muhammad Asif Jan1https://orcid.org/0000-0002-2733-5439Abdulaziz Aldegheishem2https://orcid.org/0000-0003-3287-5357Amjad Mehmood3https://orcid.org/0000-0003-3941-4617Nabil Alrajeh4https://orcid.org/0000-0002-1861-0582Akbar Khanan5https://orcid.org/0000-0002-2226-0001Jinnah College for Women, University of Peshawar, Peshawar, PakistanInstitute of Numerical Sciences, Kohat University of Science and Technology, Kohat, PakistanUrban Planning Department, Traffic Safety Technologies Chair, College of Architecture and Planning, King Saud University, Riyadh, Saudi ArabiaInstitute of Computing, Kohat University of Science and Technology, Kohat, PakistanBiomedical Technology Department, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Management Information System, A’Sharqiyah Univerity, Ibra, OmanGrey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski’s information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided.https://ieeexplore.ieee.org/document/8928579/Population-based search approachesevolutionary computationunconstrained optimizationgrey wolf optimizationglobal searchMinkowski’s formula |
| spellingShingle | Rashida Adeeb Khanum Muhammad Asif Jan Abdulaziz Aldegheishem Amjad Mehmood Nabil Alrajeh Akbar Khanan Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization IEEE Access Population-based search approaches evolutionary computation unconstrained optimization grey wolf optimization global search Minkowski’s formula |
| title | Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization |
| title_full | Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization |
| title_fullStr | Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization |
| title_full_unstemmed | Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization |
| title_short | Two New Improved Variants of Grey Wolf Optimizer for Unconstrained Optimization |
| title_sort | two new improved variants of grey wolf optimizer for unconstrained optimization |
| topic | Population-based search approaches evolutionary computation unconstrained optimization grey wolf optimization global search Minkowski’s formula |
| url | https://ieeexplore.ieee.org/document/8928579/ |
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