  Through modeling human's brainstorming process, the brain storm optimization
(BSO) algorithm has become a promising population-based evolutionary algorithm.
However, BSO is pointed out that it possesses a degenerated L-curve phenomenon,
i.e., it often gets near optimum quickly but needs much more cost to improve
the accuracy. To overcome this question in this paper, an excellent direct
search based local solver, the Nelder-Mead Simplex (NMS) method is adopted in
BSO. Through combining BSO's exploration ability and NMS's exploitation ability
together, a simplex search based BSO (Simplex-BSO) is developed via a better
balance between global exploration and local exploitation. Simplex-BSO is shown
to be able to eliminate the degenerated L-curve phenomenon on unimodal
functions, and alleviate significantly this phenomenon on multimodal functions.
Large number of experimental results show that Simplex-BSO is a promising
algorithm for global optimization problems.
