JBO-OSN: A Synaptic Bio-Inspired Optimization Approach for Floatovoltaic Hybrid Energy Systems in The Tuz Gölü (Salt Lake) Region

Year 2026, Volume: 14 Issue: 1, 36 - 45, 31.01.2026

Mert Ökten

https://doi.org/10.21541/apjess.1773408

Abstract

This study introduces a novel bio-inspired metaheuristic algorithm, named JBO-OSN (Jackal–Badger–Octopus with Optimized Synaptic Network), for addressing the multi-objective optimization of a hybrid floatovoltaic–battery–diesel energy system. The target application is the Tuz Gölü (Salt Lake) region in Türkiye, where arid climatic conditions and unique resource availability present challenges for sustainable energy planning. The aim is to reduce cost, minimize carbon emissions, and ensure battery longevity in off-grid and semi-grid contexts. The system is modeled using realistic meteorological and demand profiles, incorporating water surface effects on photovoltaic performance such as reflectivity and thermal regulation. JBO-OSN is designed by integrating biological cooperation, synaptic decision-making, and chaotic dynamics to enhance exploration and convergence. The algorithm is implemented in MATLAB and benchmarked against widely used optimization techniques including PSO, GWO, and WOA. Simulation results demonstrate that JBO-OSN achieves superior convergence speed, improved solution stability, and more effective trade-offs among objectives compared to conventional swarm-based approaches. The algorithm efficiently balances system cost, emission reduction, and battery cycling stability under arid environmental conditions. JBO-OSN shows promise as a robust decision-support tool for the design and optimization of hybrid renewable energy systems in resource-constrained, arid regions. Its bio-inspired and synaptic-based framework provides advantages over traditional algorithms, supporting future applications in sustainable energy planning.

Keywords

Floatovoltaic Systems , Hybrid Energy Optimization , Bio-inspired Algorithm , Multi-objective Optimization , Synaptic Intelligence

References

• F. C. Prinsloo, P. Schmitz, and A. Lombard, “Sustainability assessment framework and methodology with trans-disciplinary numerical simulation model for analytical floatovoltaic energy system planning assessments,” Sustain. Energy Technol. Assess., vol. 47, p. 101515, 2021. [Online]. Available: https://doi.org/10.1016/j.seta.2021.101515
• N. A. S. Elminshawy, A. Osama, O. Elbaksawi, M. Arici, and N. Naeim, “Energetic, environmental and economic (3E) analysis of a CSA algorithm optimized floatovoltaic system integrated with heat sink: Modeling with experimental validation,” Eng. Anal. Bound. Elem., vol. 159, pp. 384–401, 2024. [Online]. Available: https://doi.org/10.1016/j.enganabound.2023.12.005
• A. Lenhardt, A. Alfarhan, R. Brecha, R. B. Mulford, and A. J. Schrader, “Technoeconomic investigation of metallic porous structures for passive thermal management and desalination of dual-use floatovoltaic systems,” Desalination, vol. 601, p. 118592, 2025. [Online]. Available: https://doi.org/10.1016/j.desal.2025.118592
• M. Ökten, “Metaheuristic optimization techniques in energy systems: Innovative algorithms and application areas,” J. Sustain. Eng. Appl. Technol. Develop., vol. 7, no. 2, pp. 153–171, 2024.
• X. Fan, W. Sayers, S. Zhang, Z. Han, L. Ren, and H. Chizari, “Review and classification of bio-inspired algorithms and their applications,” J. Bionic Eng., vol. 17, pp. 611–631, 2020. [Online]. Available: https://doi.org/10.1007/s42235-020-0049-9
• V. Tomar, M. Bansal, and P. Singh, “Metaheuristic algorithms for optimization: A brief review,” Eng. Proc., vol. 59, no. 1, p. 238, 2023. [Online]. Available: https://doi.org/10.3390/engproc2023059238
• P. Bayer and M. Finkel, “Evolutionary algorithms for the optimization of advective control of contaminated aquifer zones,” Water Resour. Res., vol. 40, p. W06506, 2004.
• J. Nicklow, P. Reed, D. Savić, T. Dessalegne, L. Harrell, A. Chan-Hilton, M. Karamouz, B. Minsker, A. Ostfeld, A. Singh, and E. Zechman, “State of the art for genetic algorithms and beyond in water resources planning and management,” J. Water Resour. Plann. Manage., vol. 136, pp. 412–432, 2010.
• D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes Univ., Türkiye, 2005.
• N. Zainal, A. Zain, and S. Sharif, “Overview of artificial fish swarm algorithm and its applications in industrial problems,” Appl. Mech. Mater., vol. 815, pp. 253–257, 2015.
• K. Mason, M. Duggan, E. Barrett, J. Duggan, and E. Howley, “Predicting host CPU utilization in the cloud using evolutionary neural networks,” Future Gener. Comput. Syst., vol. 86, pp. 162–173, 2018.
• D. Simon, “Biogeography-based optimization,” IEEE Trans. Evol. Comput., vol. 12, pp. 702–713, 2008.
• W. Sayers, D. Savic, and Z. Kapelan, “Performance of LEMMO with artificial neural networks for water systems optimization,” Urban Water J., vol. 16, pp. 21–32, 2019.
• C. A. C. Coello, “Twenty years of evolutionary multiobjective optimization: A historical overview of the field,” IEEE Comput. Intell. Mag., vol. 1, pp. 28–36, 2005.
• K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, pp. 182–197, 2002.
• Z. Liao, and A. Parisio, “Reliability Optimization of Multi-Energy System Considering Energy Storage Devices Effects under Weather Uncertainties,” Energies, vol. 15, pp. 696, 2022. [Online]. Available: https://doi.org/10.3390/en15030696
• H. Zhang, D. Yue, W. Yue, K. Li, and M. Yin, “MOEA/D-Based Probabilistic PBI Approach for Risk-Based Optimal Operation of Hybrid Energy System With Intermittent Power Uncertainty,” IEEE Trans. Syst. Cybr. Syst., vol. 51, no. 4, pp. 2080-2090, 2021. [Online]. Available: https://doi.org/10.1109/TSMC.2019.2931636
• Republic of Türkiye Ministry of Environment, Urbanization, and Climate Change, “Tuz Gölü özel çevre koruma bölgesi,” 2025. [Online]. Available: https://mpgm.csb.gov.tr/tuz-golu-ozel-cevre-koruma-bolgesi-i-82214. [Accessed: May 19, 2025].
• B. Akın, “Tuz Gölü Havzası’nın kuraklık analizi,” Ulusal Çevre Bilimleri Araştırma Dergisi, vol. 2, no. 1, pp. 44–56, 2019.
• M. Ökten, “Isı ve elektrik enerjisi üretimi için doğal gaza yeşil bir çözüm: Güneş enerjisi uygulamalarıyla bir örnek olay incelemesi,” Gümüşhane Univ. J. Sci., vol. 13, no. 3, pp. 702–717, 2023. [Online]. Available: https://doi.org/10.17714/gumusfenbil.123214
• K. Liu, Y. Cai, W. Sun, Q. Feng, G. Wang, and W. Yang, “Optimal configuration of a stand-alone PV/diesel/battery hybrid energy system based on modified PSO for a translational sprinkler irrigation machine applied in remote and water-scarce areas,” Energy, vol. 320, p. 135113, 2025. [Online]. Available: https://doi.org/10.1016/j.energy.2025.135113
• MathWorks Inc., “MATLAB version: 9.12.0 (R2022b),” 2022. [Online]. Available: https://www.mathworks.com. [Accessed: Apr. 10, 2025].
• Turkish State Meteorological Service (MGM), “Sıcaklık ve ışınım verileri,” 2025. [Online]. Available: https://www.mgm.gov.tr. [Accessed: May 19, 2025].
• NASA POWER Project, “Prediction of worldwide energy resources,” 2025. [Online]. Available: https://power.larc.nasa.gov. [Accessed: Oct 3, 2025].
• J. Pierezan and L. D. S. Coelho, “Coyote optimization algorithm: A new metaheuristic for global optimization problems,” in Proc. IEEE Congr. Evol. Comput. (CEC), Rio de Janeiro, Brazil, Jul. 8–13, 2018, pp. 1–8.
• E. Varol Altay and O. Altay, “Assessment of grey wolf optimizer and its variants on benchmark functions,” in Int. Conf. Comput., Intell. Data Anal. (ICCIDA) 2022: Comput. Intell., Data Anal. Appl., pp. 55–66, 2022.
• F. N. Arıcı and E. Kaya, “Comparison of meta-heuristic algorithms on benchmark functions,” in Proc. 7th Int. Symp. Innov. Technol. Eng. Sci. (ISITES2019), Şanlıurfa, Türkiye, Nov. 22–24, 2019. [Online]. Available: https://doi.org/10.33793/acperpro.02.03.41
• E. Varol Altay and O. Altay, “Güncel metasezgisel optimizasyon algoritmalarının CEC2020 test fonksiyonları ile karşılaştırılması,” Dicle Univ. J. Eng., vol. 12, no. 5, pp. 729–741, 2021.
• K. S. Hayibo and J. M. Pearce, “Foam-based floatovoltaics: A potential solution to disappearing terminal natural lakes,” Renew. Energy, vol. 188, pp. 859–872, 2022. [Online]. Available: https://doi.org/10.1016/j.renene.2022.02.085
• D. Wang, D. Tan, and L. Liu, “Particle swarm optimization algorithm: An overview,” Soft Comput., vol. 22, no. 2, pp. 387–408, 2018. [Online]. Available: https://doi.org/10.1007/s00500-016-2474-6
• S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014. [Online]. Available: https://doi.org/10.1016/j.advengsoft.2013.12.007
• W. Yang, K. Xia, S. Fan, W. Li, T. Li, J. Zhang, and Y. Feng, “A multi-strategy whale optimization algorithm and its application,” Eng. Appl. Artif. Intell., vol. 108, p. 104558, 2022. [Online]. Available: https://doi.org/10.1016/j.engappai.2021.104558