A Comparative Study of Optimization Techniques for Model Order Reduction

Model order reduction (MOR) plays a key role in simplifying complex dynamic systems while retaining essential characteristics. This study investigates five advanced optimization techniques for MOR: the jaya optimization algorithm, particle swarm optimization (PSO), grey wolf optimization (GWO), genetic algorithm (GA), and bat algorithm. Two sample systems are selected to demonstrate order reduction. Each algorithm's effectiveness is evaluated based on its ability to minimize system errors and sustain critical system dynamics. To comprehensively assess the optimization approaches, the study incorporates several performance metrics, including system stability, integral-absolute-error (IAE), and integral-squared-error (ISE). The results highlight the strengths and limitations of each technique in terms of accuracy.