An improved fitness distance balance H5N1 optimization algorithm for seismic design problems

Purpose This study aims to develop an improved metaheuristic algorithm, FDB-SH5N1, by integrating the Fitness-Distance Balance (FDB) strategy into the SH5N1 framework to optimize seismic design of steel frame structures. The goal is to enhance convergence, stability, and global search ability in high-dimensional, nonlinear, and constrained optimization problems, ensuring structural safety and material efficiency in compliance with LRFD-AISC standards. Design/methodology/approach The proposed FDB-SH5N1 algorithm integrates the Fitness-Distance Balance (FDB) selection mechanism into the viral-inspired SH5N1 metaheuristic to dynamically balance exploration and exploitation. The algorithm was implemented in MATLAB and validated on two benchmark seismic design problems involving four-story steel frames with 132 and 428 members. Design constraints followed LRFD-AISC and ASCE 7–10 standards. Performance was evaluated based on structural weight, convergence speed, and stability, and compared against several state-of-the-art metaheuristics, including PSO, GWO, QIO, COA, AOA, SH5N1, and AFDB-ARO. Findings The FDB-SH5N1 algorithm outperformed classical and advanced metaheuristics in optimizing seismic steel frame designs. It achieved up to 96.67% reduction in structural weight compared to PSO and 9.33% over its SH5N1 predecessor, while maintaining compliance with LRFD-AISC constraints. The algorithm demonstrated faster convergence, superior stability, and consistent performance across 30 independent runs. Results confirmed its effectiveness in handling high-dimensional, constrained, and multimodal design problems, especially in preventing premature convergence and improving global search efficiency through the integration of the Fitness-Distance Balance mechanism. Originality/value This study presents the first integration of the Fitness-Distance Balance (FDB) mechanism into the SH5N1 algorithm, resulting in a novel hybrid approach (FDB-SH5N1) tailored for seismic structural optimization. Unlike existing methods, the proposed algorithm enhances both exploration and exploitation through adaptive selection and mutation strategies inspired by viral dynamics. It offers a significant advancement in solving large-scale, constrained, and multimodal structural design problems. The algorithm’s ability to produce lighter, code-compliant designs with improved convergence and stability highlights its value for engineering applications requiring robust and efficient seismic optimization.