Purpose <p>This study presents a comparative performance assessment of ten swarm intelligence-based metaheuristic optimization algorithms (SIAs), namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Crow Search Algorithm (CSA), Cuckoo Search (CS), Dophin Search Algorithm (DSA), Firefly Algorithm (FA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA) for optimizing the design parameters of Lead Core Rubber Bearing (LCRB) in base-isolated (BI) buildings under multiple seismic records.</p> Methodology <p>A comprehensive numerical simulation was performed on a six-story base-isolated building, employing seven distinct historical earthquake ground motion records to assess its seismic response characteristics. Furthermore, a regression-based multi-objective optimization model was developed to minimize critical structural responses, including roof displacement, roof acceleration, bearing displacement, and base shear, of base-isolated building.</p> Result &amp; Conclusion <p>The results revealed that the optimal LCRB design parameters derived from the different algorithms exhibited negligible variation. However, ABC, CSA, CS, and PSO demonstrated superior performance in terms of accuracy, convergence speed, computational efficiency, and reliability. Additionally, the findings demonstrate that implementing the optimized LCRB parameters led to substantial reductions in seismic demands compared to fixed-base buildings.</p>