Sperm swarm optimization for many objective power flow problems with enhanced performance evaluation in power systems

This 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.