The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach
Owing to the no free lunch theorem, no single optimisation algorithm can solve all optimisation problems accurately, so new optimisation techniques are required. In this paper, a novel metaheuristic called the deep sleep optimiser (DSO) is proposed. The deep sleep optimiser mimics the sleeping patte...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10190587/ |
| _version_ | 1797743470435106816 |
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| author | Sunday O. Oladejo Stephen O. Ekwe Lateef A. Akinyemi Seyedali A. Mirjalili |
| author_facet | Sunday O. Oladejo Stephen O. Ekwe Lateef A. Akinyemi Seyedali A. Mirjalili |
| author_sort | Sunday O. Oladejo |
| collection | DOAJ |
| description | Owing to the no free lunch theorem, no single optimisation algorithm can solve all optimisation problems accurately, so new optimisation techniques are required. In this paper, a novel metaheuristic called the deep sleep optimiser (DSO) is proposed. The deep sleep optimiser mimics the sleeping patterns of humans to solve optimisation problems. The DSO is modelled on the rise and fall of homeostatic pressure during the human sleep process. Human sleep is often modelled on the four sleep stages and the deep sleep stage is employed in this work. The mathematical model of sleep homeostatic pressure is employed to simulate and determine the deep sleep state. The performance of DSO is demonstrated by employing 23 traditional functions (i.e., unimodal, multimodal, and fixed multi-modal functions), six composite functions, three engineering design problems, two knapsack problems, and six widely known travelling salesman’s problems. Additionally, the performance is evaluated in terms of accuracy, computational running time, the Wilcoxon rank sum, and the Friedman test. Lastly, the DSO is compared with 11 other metaheuristics, including GA, PSO, TLBO, and GWO. The DSO fares comparably well and, in most instances, it outperforms other metaheuristics. |
| first_indexed | 2024-03-12T14:55:53Z |
| format | Article |
| id | doaj.art-b118723dafce4c5fb8f8c5100bd47c07 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:55:53Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b118723dafce4c5fb8f8c5100bd47c072023-08-14T23:00:29ZengIEEEIEEE Access2169-35362023-01-0111836398366510.1109/ACCESS.2023.329810510190587The Deep Sleep Optimizer: A Human-Based Metaheuristic ApproachSunday O. Oladejo0https://orcid.org/0000-0001-9573-5189Stephen O. Ekwe1Lateef A. Akinyemi2https://orcid.org/0000-0003-4207-9880Seyedali A. Mirjalili3https://orcid.org/0000-0002-1443-9458School for Data Science and Computational Thinking, University of Stellenbosch, Stellenbosch, South AfricaDepartment of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Cape Town, South AfricaDepartment of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Epe Campus, Lagos, NigeriaCentre for Artificial Intelligence Research and Optimisation, Torrens University, Brisbane, QLD, AustraliaOwing to the no free lunch theorem, no single optimisation algorithm can solve all optimisation problems accurately, so new optimisation techniques are required. In this paper, a novel metaheuristic called the deep sleep optimiser (DSO) is proposed. The deep sleep optimiser mimics the sleeping patterns of humans to solve optimisation problems. The DSO is modelled on the rise and fall of homeostatic pressure during the human sleep process. Human sleep is often modelled on the four sleep stages and the deep sleep stage is employed in this work. The mathematical model of sleep homeostatic pressure is employed to simulate and determine the deep sleep state. The performance of DSO is demonstrated by employing 23 traditional functions (i.e., unimodal, multimodal, and fixed multi-modal functions), six composite functions, three engineering design problems, two knapsack problems, and six widely known travelling salesman’s problems. Additionally, the performance is evaluated in terms of accuracy, computational running time, the Wilcoxon rank sum, and the Friedman test. Lastly, the DSO is compared with 11 other metaheuristics, including GA, PSO, TLBO, and GWO. The DSO fares comparably well and, in most instances, it outperforms other metaheuristics.https://ieeexplore.ieee.org/document/10190587/Optimisationmetaheuristicsdeep sleepREMnon-REM |
| spellingShingle | Sunday O. Oladejo Stephen O. Ekwe Lateef A. Akinyemi Seyedali A. Mirjalili The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach IEEE Access Optimisation metaheuristics deep sleep REM non-REM |
| title | The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach |
| title_full | The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach |
| title_fullStr | The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach |
| title_full_unstemmed | The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach |
| title_short | The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach |
| title_sort | deep sleep optimizer a human based metaheuristic approach |
| topic | Optimisation metaheuristics deep sleep REM non-REM |
| url | https://ieeexplore.ieee.org/document/10190587/ |
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