Mbasso, Wulfran FendziHarrison, AmbeJangir, PradeepDagal, IdrissKotb, HossamAlombah, Njimboh HenryKumar, Raman2026-01-312026-01-3120252045-2322https://doi.org./10.1038/s41598-025-99330-zhttps://hdl.handle.net/20.500.12662/10675This research presents an advanced optimization framework motivated from biological sources using the Sperm Swarm Optimization (SSO) algorithm to specifically deal with the Many-Objective Optimal Power Flow (MaO-OPF) problem in power systems. Despite the great progress made in multi-objective optimization, convergence, diversity, and computational efficiency problems still exist-in particular, the high dimensional, multifaceted, conflicting objectives space. The proposed MaOSSO algorithm incorporates adaptive diversity mechanisms along with swarm intelligent hyper-dynamic control to address these shortcomings and improve the solution quality in higher scalable architectures. This framework is extensively tested with cutting-edge algorithms NSGA-III and RVEA on the DTLZ and MaF test suites and later validated on the realistic IEEE 30, 57, and 118-bus power systems. MaOSSO is shown to consistently outperform competing methods with up to 15-20% faster convergence and 25% less computation time. While applying the algorithm on the MaO-OPF problem, the active/reactive power loss minimization was optimized along with the voltage stability, emissions, operational cost, and Pareto front diversity sustaining. The biologically inspired multi-directional search strategy incorporated in MaOSSO that provides balance between exploration and exploitation is what distinguishes this approach from others. Additional comparisons with OPF models based on FACTS and fuzzy-evolutionary OPF models demonstrate the claimed advantages in practical applications. Comprehensive multi-metric evaluation supporting the performance increase is attributed to Hypervolume (HV), Inverted Generational Distance, Generational Distance, Spread, and efficiency of runtime. A single radar plot and a cumulative ranking summary illustrate and quantify how MaOSSO outperforms more recent swarm-based algorithms like GWO, MOPSO, and MOGWO. The study describes specific future improvement actions while admitting some constraints on extremely large-scale systems. In summary, MaOSSO stands out as the most robust and flexible approach to enabling adaptive intelligent and sustainable operations on power systems.eninfo:eu-repo/semantics/openAccessSperm swarm optimization (SSO)Many-objective optimal power flow (MaO-OPF)Multi-objective optimizationReactive power loss minimizationIEEE bus system validationFlexible AC transmission systems (FACTS)Fuzzy decision frameworkComparative analysisArticle10.1038/s41598-025-99330-z2-s2.0-105005425513140383723Q115WOS:001490299100008Q1