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Marine predator inspired naked mole-rat algorithm for global optimization

Rohit Salgotra, Supreet Singh Orcid Logo, Urvinder Singh, Seyedali Mirjalili, Amir H. Gandomi

Expert Systems with Applications, Volume: 212, Start page: 118822

Swansea University Author: Rohit Salgotra

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DOI (Published version): 10.1016/j.eswa.2022.118822

Abstract

This paper proposes a hybrid version of marine predator algorithm (MPA) and naked mole-rat algorithm (NMRA) to aggregate the strengths of both algorithms. The new proposed algorithm is named as MpNMRA and designed to overcome the inherent drawbacks of MPA (slow exploitation) and NMRA (limited explor...

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Published in: Expert Systems with Applications
ISSN: 0957-4174
Published: Elsevier BV 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa61419
first_indexed 2022-10-25T16:48:47Z
last_indexed 2023-04-15T03:19:47Z
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spelling 2023-04-14T15:11:55.2655641 v2 61419 2022-10-05 Marine predator inspired naked mole-rat algorithm for global optimization 55bb4bb2f4739cc0030739621858ca47 Rohit Salgotra Rohit Salgotra true false 2022-10-05 MACS This paper proposes a hybrid version of marine predator algorithm (MPA) and naked mole-rat algorithm (NMRA) to aggregate the strengths of both algorithms. The new proposed algorithm is named as MpNMRA and designed to overcome the inherent drawbacks of MPA (slow exploitation) and NMRA (limited exploration). The algorithm adds the basic structure of MPA to the worker phase of NMRA, while keeping all the major parameters of both the algorithm. The major parameters of both the algorithms are subjected to four different mutation strategies namely, exponential, linear, simulated annealing and logarithmic mutation strategies. The concept of simulated annealing-based mutation is found to be best for most of the parameters, whereas in some cases exponentially decreasing weights provide better results. Leveraging on the best mutation strategies for all the parameters, the proposed MpNMRA is tested on CEC2005, CEC2014 and CEC 2019 benchmark problems. The experimental results demonstrate that MpNMRA provides best results when compared to other algorithms in the literature on higher-dimensional problems. This work also considers solving three real-world optimization problems and training of a multi-layer perceptron using the proposed algorithm. Statistical results obtained from Wilcoxon’s rank-sum test, Freidman’s test and computational complexity further proves that the proposed algorithm is highly efficient and provide superior results. Journal Article Expert Systems with Applications 212 118822 Elsevier BV 0957-4174 Marine predator algorithm; Naked mole-rat algorithm; Hybrid algorithms; Self-adaptivity; Numerical optimization 1 2 2023 2023-02-01 10.1016/j.eswa.2022.118822 http://dx.doi.org/10.1016/j.eswa.2022.118822 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2023-04-14T15:11:55.2655641 2022-10-05T08:22:12.5290212 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Rohit Salgotra 1 Supreet Singh 0000-0002-8383-6799 2 Urvinder Singh 3 Seyedali Mirjalili 4 Amir H. Gandomi 5
title Marine predator inspired naked mole-rat algorithm for global optimization
spellingShingle Marine predator inspired naked mole-rat algorithm for global optimization
Rohit Salgotra
title_short Marine predator inspired naked mole-rat algorithm for global optimization
title_full Marine predator inspired naked mole-rat algorithm for global optimization
title_fullStr Marine predator inspired naked mole-rat algorithm for global optimization
title_full_unstemmed Marine predator inspired naked mole-rat algorithm for global optimization
title_sort Marine predator inspired naked mole-rat algorithm for global optimization
author_id_str_mv 55bb4bb2f4739cc0030739621858ca47
author_id_fullname_str_mv 55bb4bb2f4739cc0030739621858ca47_***_Rohit Salgotra
author Rohit Salgotra
author2 Rohit Salgotra
Supreet Singh
Urvinder Singh
Seyedali Mirjalili
Amir H. Gandomi
format Journal article
container_title Expert Systems with Applications
container_volume 212
container_start_page 118822
publishDate 2023
institution Swansea University
issn 0957-4174
doi_str_mv 10.1016/j.eswa.2022.118822
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1016/j.eswa.2022.118822
document_store_str 0
active_str 0
description This paper proposes a hybrid version of marine predator algorithm (MPA) and naked mole-rat algorithm (NMRA) to aggregate the strengths of both algorithms. The new proposed algorithm is named as MpNMRA and designed to overcome the inherent drawbacks of MPA (slow exploitation) and NMRA (limited exploration). The algorithm adds the basic structure of MPA to the worker phase of NMRA, while keeping all the major parameters of both the algorithm. The major parameters of both the algorithms are subjected to four different mutation strategies namely, exponential, linear, simulated annealing and logarithmic mutation strategies. The concept of simulated annealing-based mutation is found to be best for most of the parameters, whereas in some cases exponentially decreasing weights provide better results. Leveraging on the best mutation strategies for all the parameters, the proposed MpNMRA is tested on CEC2005, CEC2014 and CEC 2019 benchmark problems. The experimental results demonstrate that MpNMRA provides best results when compared to other algorithms in the literature on higher-dimensional problems. This work also considers solving three real-world optimization problems and training of a multi-layer perceptron using the proposed algorithm. Statistical results obtained from Wilcoxon’s rank-sum test, Freidman’s test and computational complexity further proves that the proposed algorithm is highly efficient and provide superior results.
published_date 2023-02-01T05:02:15Z
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score 11.090464

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