An improved Crow Search Algorithm for high-dimensional problems

Abstract

Crow search algorithm (CSA) is a recently proposed metaheuristic optimizer inspired by the intelligent behaviour of crows with attributes like simplicity and ease of implementation. CSA is claimed superior and more effective in optimizing a variety of constrained engineering design problems in comparison to other state-of-art algorithms. In the present work, CSA is applied to high dimensional optimization problems and it is found that CSA suffers from premature convergence which leads to lower precision and less accuracy in optimization or sometimes failure. Therefore an improvement in CSA (ICSA) is suggested to solve high-dimensional global optimization problems efficiently. The balance between exploitation and exploration capabilities of CSA is improved by introducing experience factor, adaptive adjustment operator and Lévy flight distribution in position updating mechanism of crows. Lévy flight distribution promotes continuous exploration of search space and prevents premature convergence by escaping from local optimum at any stage. The performance of ICSA is validated on high-dimensional nonlinear scalable benchmark test functions. The proposed improvement in CSA makes it highly competitive and less sensitive to function dimensions. ICSA is also found superior to other well established optimizers.