Enhancing Parameter Estimation in Modified Spencer Models Using the Firefly Optimization Algorithm

Optimization algorithms are critical in solving complex engineering problems where parameter estimation is crucial. Among various metaheuristic algorithms, the Firefly Optimization Algorithm (FOA) has gained significant attention due to its simplicity and effectiveness in solving multimodal optimization problems. The objective of this paper is to evaluate the performance of the Firefly Optimization Algorithm (FOA) for parameter estimation in Modified Spencer Models, a specific type of Bouc-Wen hysteresis model used in various engineering applications, particularly in modeling and controlling magneto-rheological (MR) dampers. This study demonstrates the algorithm's effectiveness in estimating parameters with high accuracy and minimal computational effort. Additionally, its performance will be compared with the widely used Particle Swarm Optimization (PSO) algorithm. The results indicate that although the Firefly Optimization Algorithm (FOA) efficiently handles multimodal optimization tasks, making it a valuable tool in engineering and applied sciences, the Particle Swarm Optimization (PSO) algorithm performs better in minimizing error and achieving faster convergence.