Tackling global optimization problems with a novel algorithm - Mouth Brooding Fish algorithm

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 optimization algorithm inspired by Mouth Brooding Fish in nature. Meta-heuristics based on evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques. Mouth Brooding Fish (MBF) algorithm simulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. The proposed algorithm uses the movement, dispersion and protection behavior of Mouth Brooding Fish as a pattern to find the best possible answer. This algorithm is evaluated by CEC2013& 14 benchmark functions for single objective optimization and the proposed algorithm competes with the advanced algorithms (CMA-ES, JADE, SaDE, and GL-25). The results demonstrate that the proposed algorithm is able to construct very promising results and has merits in solving challenging optimization problems. (C) 2017 Elsevier B.V. All rights reserved.