A Hybrid Owl Search Algorithm with Lévy Flights, Opposition-Based Learning, and Adaptive Memory for Continuous Optimization

Awaz Ahmed Shaban; Subhi R. M. Zeebaree

doi:10.65542/djei.v2i3.52

Authors

Awaz Ahmed Shaban Information Technology Department, Technical College of Duhok, Duhok Polytechnic University, Duhok, Iraq
Subhi R. M. Zeebaree Cybersecurity Engineering Department, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq

DOI:

https://doi.org/10.65542/djei.v2i3.52

Keywords:

Owl Search Algorithm, hybrid metaheuristics, Lévy flights, opposition-based learning, adaptive memory, benchmark optimization

Abstract

This study develops and evaluates a selective adaptive hybrid variant of the Owl Search Algorithm (OSA) for bounded continuous optimization. The proposed framework integrates Lévy-flight perturbation, opposition-based learning (OBL), and adaptive memory guidance within an operator-coordination mechanism designed to regulate the balance between diversification and intensification. Its contribution is not merely the aggregation of established enhancement operators; rather, it lies in examining whether adaptive operator scheduling can produce more stable performance than deterministic serial hybridization when the same operator set is used. Six OSA-derived variants are evaluated under a unified benchmark protocol: the canonical OSA, OSA with Lévy flights, OSA with OBL, OSA with memory guidance, a selection-based hybrid, and a serial hybrid. Experiments on 23 standard continuous benchmark functions indicate that the selection-based hybrid achieves the strongest aggregate profile, with an average rank of 2.326 and 12 best/tied-best function outcomes, followed by the serial hybrid with an average rank of 2.609 and seven best/tied-best outcomes. The results suggest that state-responsive coordination can improve robustness across heterogeneous landscapes by allocating search effort according to diversity, stagnation, and recent improvement history. The study also identifies the current validation boundary of the work and outlines the additional external benchmarking and application-oriented experiments required for broader claims of general competitiveness.

References

1. Rustamova, N.; Vivekanantharasa, R. Comprehensive review and hybrid evolution of teaching–learning-based optimization. Qubahan Techno J. 2023, 2, 1–13. https://doi.org/10.48161/qtj.v2n2a19.

2. Almufti, S.M. Metaheuristics Algorithms: Overview, Applications, and Modifications, 1st ed.; Deep Science Publishing, 2025. https://doi.org/10.70593/978-93-7185-454-2.

3. Masoud, R.; Kuchkarova, F.M. Metaheuristics in sustainable and green optimization. Qubahan Techno J. 2023, 2, 1–17. https://doi.org/10.48161/qtj.v2n3a20.

4. Almufti, S.M.; Sallow, A.B. Benchmarking metaheuristics: Comparative analysis of PSO, GWO, and ESNS on complex optimization landscapes. AVE Trends Intell. Comput. Syst. 2025, 2, 62–76. https://doi.org/10.64091/ATICS.2025.0000131.

5. Chawla, M.; Duhan, M. Lévy flights in metaheuristics optimization algorithms—A review. Appl. Artif. Intell. 2018, 32. https://doi.org/10.1080/08839514.2018.1508807.

6. Tizhoosh, H.R. Opposition-based learning: A new scheme for machine intelligence. In Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2005. https://doi.org/10.1109/CIMCA.2005.1631345.

7. Xu, Q.; Wang, L.; Wang, N.; Hei, X.; Zhao, L. A review of opposition-based learning from 2005 to 2012. Eng. Appl. Artif. Intell. 2014. https://doi.org/10.1016/j.engappai.2013.12.004.

8. Taillard, É.D.; Gambardella, L.M.; Gendreau, M.; Potvin, J.Y. Adaptive memory programming: A unified view of metaheuristics. Eur. J. Oper. Res. 2001, 135. https://doi.org/10.1016/S0377-2217(00)00268-X.

9. Farahmand-Tabar, S. Memory-driven metaheuristics: Improving optimization performance. In Handbook of Formal Optimization; Springer Nature Singapore: Singapore, 2023; pp. 1–26. https://doi.org/10.1007/978-981-19-8851-6_38-1.

10. Jain, M.; Maurya, S.; Rani, A.; Singh, V. Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization. J. Intell. Fuzzy Syst. 2018, 34, 1573–1582. https://doi.org/10.3233/JIFS-169452.

11. Cao, Y.; Wang, Q.; Wang, Z.; Jermsittiparsert, K.; Shafiee, M. A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Rep. 2020, 6. https://doi.org/10.1016/j.egyr.2020.01.010.

12. Li, D.; Deng, L.; Su, Q.; Song, Y. Providing a guaranteed power for the BTS in telecom tower based on improved balanced owl search algorithm. Energy Rep. 2020, 6. https://doi.org/10.1016/j.egyr.2020.01.006.

13. Storn, R.; Price, K. Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 1997, 11, 341–359. https://doi.org/10.1023/A:1008202821328.

14. Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the ICNN’95—International Conference on Neural Networks, 1995; pp. 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.

15. Mirjalili, S.; Mirjalili, S.M.; Lewis, A. Grey wolf optimizer. Adv. Eng. Softw. 2014, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007.

16. Mirjalili, S.; Lewis, A. The whale optimization algorithm. Adv. Eng. Softw. 2016, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008.

17. Heidari, A.A.; Mirjalili, S.; Faris, H.; Aljarah, I.; Mafarja, M.; Chen, H. Harris hawks optimization: Algorithm and applications. Future Gener. Comput. Syst. 2019, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028.

18. Faramarzi, A.; Heidarinejad, M.; Stephens, B.; Mirjalili, S. Equilibrium optimizer: A novel optimization algorithm. Knowl.-Based Syst. 2020, 191, 105190. https://doi.org/10.1016/j.knosys.2019.105190.

19. Li, S.; Chen, H.; Wang, M.; Heidari, A.A.; Mirjalili, S. Slime mould algorithm: A new method for stochastic optimization. Future Gener. Comput. Syst. 2020, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055.

20. Pei, J.; Mei, Y.; Liu, J.; Zhang, M.; Yao, X. Adaptive operator selection for meta-heuristics: A survey. IEEE Trans. Artif. Intell. 2025, 6, 1991–2012. https://doi.org/10.1109/TAI.2025.3545792.

21. Aydin, M.E.; Durgut, R.; Rakib, A.; Phung, K. Adaptive operator selection in heuristic optimization utilizing generalized experience with reinforcement learning. Int. J. Parallel Emergent Distrib. Syst. 2025, 1–31. https://doi.org/10.1080/17445760.2025.2605297.

22. Song, Y.; et al. Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities. Swarm Evol. Comput. 2024, 86, 101517. https://doi.org/10.1016/j.swevo.2024.101517.

23. Guo, H.; Ma, Y.; Ma, Z.; Chen, J.; Zhang, X.; Cao, Z.; Zhang, J.; Gong, Y.-J. Deep reinforcement learning for dynamic algorithm selection: A proof-of-principle study on differential evolution. IEEE Trans. Syst. Man Cybern. Syst. 2024, 54, 4247–4259. https://doi.org/10.1109/TSMC.2024.3374889.

24. Guo, H.; Ma, S.; Huang, Z.; Hu, Y.; Ma, Z.; Zhang, X.; Gong, Y.-J. Reinforcement learning-based self-adaptive differential evolution through automated landscape feature learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 2025; pp. 1117–1126. https://doi.org/10.1145/3712256.3726309.

25. Gil, M.; Akbari, E.; Rahimnejad, A.; Ghasemi, M.; Gadsden, S.A. Solution of optimal reactive power dispatch by Lévy-flight phasor particle swarm optimization. Intell. Syst. Appl. 2024, 23, 200398. https://doi.org/10.1016/j.iswa.2024.200398.

26. Papanikolaou, C.; Sharma, A.; Lind, P.G.; Lencastre, P. Lévy flight model of gaze trajectories to assist in ADHD diagnoses. Entropy 2024, 26, 392. https://doi.org/10.3390/e26050392.

27. Li, J.; An, Q.; Lei, H.; Deng, Q.; Wang, G.G. Survey of Lévy flight-based metaheuristics for optimization. Mathematics 2022, 10, 2785. https://doi.org/10.3390/math10152785.

28. Gopi, S.; Mohapatra, P. Fast random opposition-based learning Aquila optimization algorithm. Heliyon 2024, 10, e26187. https://doi.org/10.1016/j.heliyon.2024.e26187.

29. Lakbichi, R.; et al. Opposition-based learning techniques in metaheuristics: Classification, comparison, and convergence analysis. PeerJ Comput. Sci. 2025, 11, e2935. https://doi.org/10.7717/peerj-cs.2935.

30. Acan, A.; Unveren, A. A shared-memory ACO+GA hybrid for combinatorial optimization. In Proceedings of the 2007 IEEE Congress on Evolutionary Computation, 2007; pp. 2078–2085. https://doi.org/10.1109/CEC.2007.4424729.

31. Zhang, Z.; Zhu, J.; Nie, F. A novel hybrid adaptive differential evolution for global optimization. Sci. Rep. 2024, 14, 19697. https://doi.org/10.1038/s41598-024-70731-w.

32. Alabdulkreem, E.; Saeed, M.K.; Alotaibi, S.S.; Allafi, R.; Mohamed, A.; Hamza, M.A. Bone cancer detection and classification using owl search algorithm with deep learning on X-ray images. IEEE Access 2023, 11. https://doi.org/10.1109/ACCESS.2023.3319293.

33. El-Ashmawi, W.H.; Abd Elminaam, D.S.; Nabil, A.M.; Eldesouky, E. A chaotic owl search algorithm-based bilateral negotiation model. Ain Shams Eng. J. 2020, 11. https://doi.org/10.1016/j.asej.2020.01.005.

34. Chao, I.-M.; Hsiung, S.-C.; Liu, J.-L. Selecting proper population-based metaheuristic algorithms for the different modals of unconstrained optimization problems. Int. J. Organ. Innov. 2022, 14, 416–431.

35. Mirjalili, S.; Mirjalili, S.M.; Hatamlou, A. Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Comput. Appl. 2016, 27, 495–513. https://doi.org/10.1007/s00521-015-1870-7.

36. Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl.-Based Syst. 2015, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006.

37. Yang, X.-S.; Deb, S. Cuckoo search via Lévy flights. In Proceedings of the World Congress on Nature and Biologically Inspired Computing, 2009.

38. Mirjalili, S.; Gandomi, A.H.; Mirjalili, S.Z.; Saremi, S.; Faris, H.; Mirjalili, S.M. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv. Eng. Softw. 2017, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002.

39. Mirjalili, S. SCA: A sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 2016, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022.