Brain storm optimization (BSO) is a newly proposed population-based optimization algorithm, which uses a logarithmic sigmoid transfer function to adjust its search range during the convergent process. However, this adjustment only varies with the current iteration number and lacks of flexibility and variety which makes a poor search effciency and robustness of BSO. To alleviate this problem, an adaptive step length structure together with a success memory selection strategy is proposed to be incorporated into BSO. This proposed method, adaptive step length based on memory selection BSO, namely ASBSO, applies multiple step lengths to modify the generation process of new solutions, thus supplying a flexible search according to corresponding problems and convergent periods. The novel memory mechanism, which is capable of evaluating and storing the degree of improvements of solutions, is used to determine the selection possibility of step lengths. A set of 57 benchmark functions are used to test ASBSOs search ability, and four real-world problems are adopted to show its application value. All these test results indicate the remarkable improvement in solution quality, scalability, and robustness of ASBSO.
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