A metaheuristic optimization framework inspired by virus mutations and its ability to optimize the structural design of 2D and 3D steel frames compared to other methods

Traditional metaheuristic approaches for structural optimization often encounter challenges such as premature convergence, computational inefficiency, and limited adaptability to complex constraints in high-dimensional search spaces. To address these limitations, this study proposes the mutation-based virus pandemic optimization (MVPO) algorithm, a novel bio-inspired metaheuristic that emulates virus transmission dynamics and mutation mechanisms. MVPO integrates population-based exploration, simulating viral spread through intra-community interactions (guided by wavelet functions to model transmission variability) and inter-community propagation, with a two-phase mutation strategy: the first phase enhances global exploration via elongated search steps during accelerated spread, while the second phase introduces structural perturbations to escape local optima. The algorithm’s efficacy is evaluated through three benchmark problems, two planar (2D) frames and a three-dimensional (3D) space frame, with comparisons against established methods (e.g., particle swarm optimization, cuckoo search) and contemporary algorithms (e.g., marine predators algorithm, pelican optimization algorithm). Results demonstrate MVPO’s superior performance, achieving weight reductions of 0.18 %–24.2 % across all structures, including the complex 3D frame, while requiring 11.2 %–61.62 % fewer structural analyses than competitors. Reliability is evidenced by minimal standard deviations (7.88–69.58) across multiple runs, indicating consistent convergence to near-optimal solutions. Stress-ratio analyses confirm adherence to design constraints, ensuring feasible lightweight configurations. These findings establish MVPO as a computationally efficient and robust tool for structural optimization, capable of navigating intricate 2D and 3D design spaces with enhanced adaptability. The algorithm’s bio-inspired mechanics bridge biological principles with engineering precision, offering practical advancements for complex optimization challenges.