Simplex Search Based Brain Storm Optimization
This work addresses a specific bottleneck in evolutionary algorithms for global optimization, offering an incremental improvement for researchers in optimization and computational intelligence.
The paper tackled the degenerated L-curve phenomenon in brain storm optimization (BSO), where it converges quickly but struggles to improve accuracy, by integrating the Nelder-Mead Simplex method to balance exploration and exploitation. The resulting Simplex-BSO eliminated this phenomenon on unimodal functions and significantly alleviated it on multimodal functions, as shown through extensive experiments.
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.