Modified Krill Herd Algorithm for Global Numerical Optimization Problems

Abstract

For the purpose of improving the search strategy of the krill herd algorithm (KHA) , an improved robust approach is proposed to address the function optimization problems, namely, modified krill herd algorithm (MKHA) . In MKHA method, the modification of krill herd algorithm focuses on genetic operators (GOs) and it occurs in the ordering of procedures of the basic krill herd algorithm, where the crossover and mutation operators are employed after the updating process of the krill individuals position, the krill herd (KH) motion calculations, is finished. This modification is conducted because the genetic operators insignificantly exploit to enhance the global exploration search in the basic krill herd algorithm so as to speed up convergence. Several versions of benchmark functions are applied to verify the proposed method (MKHA) and it is showed that, in most cases, the proposed algorithm (MKHA) obtained better results in comparison with the basic KHA and other comparative methods.