Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test
The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization Algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the Lévy or Brownian approach for its...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824003065 |
| _version_ | 1797234019382853632 |
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| author | Manish Kumar Kanchan Rajwar Kusum Deep |
| author_facet | Manish Kumar Kanchan Rajwar Kusum Deep |
| author_sort | Manish Kumar |
| collection | DOAJ |
| description | The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization Algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the Lévy or Brownian approach for its foraging strategy. Despite its acclaim, the structural bias within MPA has not been thoroughly investigated, marking a significant gap in the current research. This absence of targeted research forms the core rationale behind initiating this study. Structural bias has recently been identified in NIOAs, causing the population to revisit specific regions of the search space without gaining new information. As a result, it may lead to increased computational costs and slow down the rate of convergence. Therefore, identifying structural bias is essential to better understand the search mechanism of MPA. To ascertain the presence of any structural bias, two recently introduced models are employed: the BIAS toolbox and the Generalized Signature Test. These examinations reveal a notable structural bias in MPA, predominantly towards the center of the search space. Also, possible future research directions for MPA are discussed. Our findings provide valuable insights into the search dynamics of the algorithm, fostering the development of new, unbiased, and efficient algorithms. |
| first_indexed | 2024-04-24T16:25:24Z |
| format | Article |
| id | doaj.art-1546644ed78e46e6bb9f79160106d199 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-24T16:25:24Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-1546644ed78e46e6bb9f79160106d1992024-03-31T04:37:10ZengElsevierAlexandria Engineering Journal1110-01682024-05-01953849Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature TestManish Kumar0Kanchan Rajwar1Kusum Deep2Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, IndiaDepartment of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, IndiaDepartment of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India; Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, 247667, India; Corresponding author at: Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India.The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization Algorithm (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using the Lévy or Brownian approach for its foraging strategy. Despite its acclaim, the structural bias within MPA has not been thoroughly investigated, marking a significant gap in the current research. This absence of targeted research forms the core rationale behind initiating this study. Structural bias has recently been identified in NIOAs, causing the population to revisit specific regions of the search space without gaining new information. As a result, it may lead to increased computational costs and slow down the rate of convergence. Therefore, identifying structural bias is essential to better understand the search mechanism of MPA. To ascertain the presence of any structural bias, two recently introduced models are employed: the BIAS toolbox and the Generalized Signature Test. These examinations reveal a notable structural bias in MPA, predominantly towards the center of the search space. Also, possible future research directions for MPA are discussed. Our findings provide valuable insights into the search dynamics of the algorithm, fostering the development of new, unbiased, and efficient algorithms.http://www.sciencedirect.com/science/article/pii/S1110016824003065Metaheuristic algorithmNature-Inspired Optimization Algorithm (NIOA)Marine Predator Algorithm (MPA)Structural biasBIAS toolboxGeneralized Signature Test (GST) |
| spellingShingle | Manish Kumar Kanchan Rajwar Kusum Deep Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test Alexandria Engineering Journal Metaheuristic algorithm Nature-Inspired Optimization Algorithm (NIOA) Marine Predator Algorithm (MPA) Structural bias BIAS toolbox Generalized Signature Test (GST) |
| title | Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test |
| title_full | Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test |
| title_fullStr | Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test |
| title_full_unstemmed | Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test |
| title_short | Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test |
| title_sort | analysis of marine predators algorithm using bias toolbox and generalized signature test |
| topic | Metaheuristic algorithm Nature-Inspired Optimization Algorithm (NIOA) Marine Predator Algorithm (MPA) Structural bias BIAS toolbox Generalized Signature Test (GST) |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824003065 |
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