A novel hybrid of Nonlinear Sine Cosine Algorithm and Safe Experimentation Dynamics for model order reduction

The current study introduces the hybridization of Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction high-order single-input single-output (SISO) systems. Reciprocated synergism between both meta-heuristic algorithms is achieved by appropriating nonlinear position-updated mechanism NSCA enhanced exploration/exploitation competencies proficiency SED in maximizing stagnation avoidance within local optima. Named NSCA-SED algorithm, applicability proposed assessed scholastic adoption sixth-order numerical transfer function towards two independent systems enclosing Double-Pendulum Overhead Crane Flexible Manipulator. results further suggested superior alternative terms execution robustness consistency excellence against other available optimization-based methods tackling reduction. Exemplified simulations sequentially demonstrated considerable improvements employment over conventional SCA following respective proportions 97.17%, 13.17% 29.03% Example 1, 2 3.