A hybrid crow search algorithm based on rough searching scheme for solving engineering optimization problems

Abstract

In this paper, a hybrid intelligent algorithm, named rough crow search algorithm (RCSA), by combining crow search algorithm (CSA) with rough searching scheme (RSS) is presented for solving engineering optimization problems. RCSA integrates the merits of the CSA and RSS to intensify the search in the promising region where the global solution resides. In terms of robustness and efficiency of the available optimization algorithms, some algorithms may not be in a position to specify the global optimal solution precisely but can rather specify them in a ‘rough sense’. Thus, the main reason for incorporating the RSS is handling the impreciseness and roughness of the available information about the global optimal, particularly for the problems with high dimensionality. By upper and lower approximations of the RST, the promising region becomes under siege. Therefore this can accelerate the optimum seeking operation and achieve the global optimum with a low computational cost. The proposed RCSA algorithm is validated on 30 benchmark problems of IEEE CEC 2005, IEEE CEC 2010 and 4 engineering design problems. The obtained results by RCSA are compared with different algorithms from the literature. The comparisons demonstrate that the RCSA outperform the other algorithms for almost all benchmark problems in terms of solution quality based on the results of statistical measures and Wilcoxon signed ranks test.