Numerous approaches have been developed to handle global optimization issues in various fields nowadays due to the growing complexity of such problems. The mouth-brooding fish seen in nature served as inspiration for the novel global optimization algorithm proposed in this study. Swarm intelligence and evolutionary computation-based metaheuristics are two well-known examples of problem-solving methods that draw inspiration from nature. The Mouth Brooding Fish (MBF) algorithm models how organisms cooperate with one another to survive and spread across an ecosystem. The suggested algorithm finds the optimal solution by using a pattern in the movement, dispersion, and protective behavior of hatchling fish in the mouth. The suggested algorithm competes with meta-hueristic algorithms according to the CEC2013&14 benchmark functions for single-objective optimization (CMA-ES, JADE, SaDE, and GL-25). Finally, the optimization of the size, shape, and topology on a number of compound and large trusses showed that the proposed algorithm is capable of producing very promising results and is competent in solving challenging optimization problems, and it can be used to optimize the trusses in civil engineering.
