LAUSR

Brain storm optimization is a newly proposed meta-heuristic that has shown great success in many applications. However, it still suffers from slow convergence speed and population premature problems. To address these issues, this paper proposes a distributed brain storm optimization algorithm, namely DBSO, for both continuous and discrete optimization applications. In DBSO, problem solutions are located on several isolated groups, and they communicate with each other by a leader and literal learning strategies. By doing so, both exploitation and exploration abilities of the algorithm can be greatly enhanced. Experiments are conducted based on a set of continuous numerical benchmark functions and discrete graph planarization instances. The extensive experiments and statistical analysis indicate that DBSO can perform better or competitive results than other methods in terms of solution accuracy and convergence speed.