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Racoon optimization algorithm

Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage...

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Main Authors: Koohi, Sina Zangbari, Abdul Hamid, Nor Asilah Wati, Othman, Mohamed, Ibragimov, Gafurjan
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2019
Online Access:http://psasir.upm.edu.my/id/eprint/81920/1/Racoon%20optimization%20algorithm.pdf
http://psasir.upm.edu.my/id/eprint/81920/
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spelling my.upm.eprints.819202020-10-17T15:30:33Z http://psasir.upm.edu.my/id/eprint/81920/ Racoon optimization algorithm Koohi, Sina Zangbari Abdul Hamid, Nor Asilah Wati Othman, Mohamed Ibragimov, Gafurjan Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the Raccoon Optimization Algorithm (ROA). The ROA is inspired by the rummaging behaviours of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviours exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA’s ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other well-known optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time. Institute of Electrical and Electronics Engineers 2019-01 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/81920/1/Racoon%20optimization%20algorithm.pdf Koohi, Sina Zangbari and Abdul Hamid, Nor Asilah Wati and Othman, Mohamed and Ibragimov, Gafurjan (2019) Racoon optimization algorithm. IEEE Access, 7. pp. 5383-5399. ISSN 2169-3536 10.1109/ACCESS.2018.2882568
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Population-based meta-heuristic is a high-level method intended to provide sufficient solution for problems with incomplete information among a massive volume of solutions. However, it does not guarantee to attain global optimum in a reasonable time. To improve the time and accuracy of the coverage in the population-based meta-heuristic, this paper presents a novel algorithm called the Raccoon Optimization Algorithm (ROA). The ROA is inspired by the rummaging behaviours of real raccoons for food. Raccoons are successful animals because of their extraordinarily sensitive and dexterous paws and their ability to find solutions for foods and remember them for up to three years. These capabilities make raccoons expert problem solvers and allow them to purposefully seek optimum solutions. These behaviours exploited in the ROA to search the solution spaces of nonlinear continuous problems to find the global optimum with higher accuracy and lower time coverage. To evaluate the ROA’s ability in addressing complicated problems, it has been tested on several benchmark functions. The ROA is then compared with nine other well-known optimization algorithms. These experiments show that the ROA achieves higher accuracy with lower coverage time.
format Article
author Koohi, Sina Zangbari
Abdul Hamid, Nor Asilah Wati
Othman, Mohamed
Ibragimov, Gafurjan
spellingShingle Koohi, Sina Zangbari
Abdul Hamid, Nor Asilah Wati
Othman, Mohamed
Ibragimov, Gafurjan
Racoon optimization algorithm
author_facet Koohi, Sina Zangbari
Abdul Hamid, Nor Asilah Wati
Othman, Mohamed
Ibragimov, Gafurjan
author_sort Koohi, Sina Zangbari
title Racoon optimization algorithm
title_short Racoon optimization algorithm
title_full Racoon optimization algorithm
title_fullStr Racoon optimization algorithm
title_full_unstemmed Racoon optimization algorithm
title_sort racoon optimization algorithm
publisher Institute of Electrical and Electronics Engineers
publishDate 2019
url http://psasir.upm.edu.my/id/eprint/81920/1/Racoon%20optimization%20algorithm.pdf
http://psasir.upm.edu.my/id/eprint/81920/
_version_ 1681490849245954048
score 13.211869

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