Hybrid Newton–Sperm Swarm Optimization Algorithm for Nonlinear Systems

Several problems have been solved by nonlinear equation systems (NESs), including real-life issues in chemistry and neurophysiology. However, the accuracy of solutions is highly dependent on efficiency algorithm used. In this paper, a Modified Sperm Swarm Optimization Algorithm called MSSO introduced to solve NESs. combines Newton’s second-order iterative method with (SSO). Through combination, MSSO’s search mechanism improved, its convergence rate accelerated, local optima are avoided, more accurate provided. The overcomes several drawbacks method, such as initial points’ selection, falling into trap optima, divergence. study, was evaluated using eight NES benchmarks that commonly used literature, three which from applications. Furthermore, compared well-known optimization algorithms, original SSO, Harris Hawk (HHO), Butterfly (BOA), Ant Lion Optimizer (ALO), Particle (PSO), Equilibrium (EO). According results, outperformed algorithms across all selected benchmark four aspects: stability, fitness values, best solutions, speed.