  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 ASBSO's 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.
