Optimization of benchmark functions and practical problems using Crow Search Algorithm

Researchers are increasingly looking towards natural phenomenon to search answers for complex real-world problems. This paper demonstrates how the intelligent behavior of crows can be utilized for getting an optimized output for complex engineering problems. The Crow Search Algorithm (CrSA) is a population based nature inspired meta-heuristic algorithm which is based on the navigation method of crows; how the crows use their intelligence in storing their food, in steeling other crow's food and saving themselves from becoming future victims. To validate the effectiveness of CrSA simulations have been performed on various mathematical benchmark functions and on some practical engineering design problem. The results obtained with the proposed algorithm have been compared with other existing meta-heuristic approaches available in literatures. This paper also shows the effect of change of control parameters on the performance of CrSA. Due to the parallel search capability, non-dependence on nature of problem, excellent direct search capability and easy MATLAB implementation, the CrSA is found to be superior to traditional mathematical techniques for real-world engineering problems.