The Deep Sleep Optimizer: A Human-Based Metaheuristic Approach

Sunday O. Oladejo, Stephen O. Ekwe, Lateef A. Akinyemi, Seyedali A. Mirjalili

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)83639-83665
Number of pages27
JournalIEEE Access
Publication statusPublished - 2023


  • deep sleep
  • metaheuristics
  • non-REM
  • Optimisation
  • REM


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