<p>This study presents a novel, nature-inspired, population-based metaheuristic algorithm called the bioluminescent fungi optimization algorithm (BFOA). The proposed algorithm is inspired by the spore dispersal strategy of bioluminescent fungi, incorporating both direct spore release and indirect dispersal via nocturnally attracted insects that carry and spread spores as they move during the day. BFOA is designed to maintain a strong balance between exploration and exploitation throughout optimization by dynamically adjusting its search behavior based on individual fitness and population diversity. The algorithm was evaluated by the CEC’2018 benchmark functions in 30, 50, and 100 dimensions and was compared to several well-known and recent metaheuristic algorithms. The experimental results demonstrate that BFOA consistently achieves superior or competitive performance in most test cases. According to Friedman’s ranking test, BFOA attained the top overall rank in all tested dimensions. Furthermore, BFOA was applied to three classical engineering design problems—tension/compression spring, three-bar truss, and pressure vessel—and outperformed the compared algorithms in all cases, confirming its robustness and practical effectiveness.</p>