Nowadays, due to the fact that difficulty of global optimization problems in different fields is increasing,various methods have been introduced to solve such problems. This paper proposes a novel global opti-mization algorithm inspired by Mouth Brooding Fish in nature. Meta-heuristics based on evolutionarycomputation and swarm intelligence are outstanding examples of nature-inspired solution techniques.Mouth Brooding Fish (MBF) algorithm simulates the symbiotic interaction strategies adopted by organ-isms to survive and propagate in the ecosystem. The proposed algorithm uses the movement, dispersionand protection behavior of Mouth Brooding Fish as a pattern to find the best possible answer. Thisalgorithm is evaluated by CEC2013&14 benchmark functions for single objective optimization and theproposed algorithm competes with the advanced algorithms (CMA-ES, JADE, SaDE, and GL-25). The resultsdemonstrate that the proposed algorithm is able to construct very promising results and has merits insolving challenging optimization problems.
