An improved crow search algorithm with multi strategy disturbance

Abstract

As a new meta heuristic intelligent algorithm, crow search algorithm simulates the behavior of crows following each other and stealing food. Due to the simplicity and robustness of crow search algorithm, it has been successfully applied in many fields. However, like other swarm intelligence optimization algorithms, crow search algorithm also has the disadvantages of slow convergence speed and easy to fall into local optimization. In order to improve the convergence accuracy and later search ability of the algorithm, a new hybrid crow search algorithm called multi strategy disturbance improved crow search algorithm (MSD-CSA) is proposed based on the traditional crow search algorithm. In MSD-CSA, the sharing mechanism is added to improve the location update mode of random tracking in the original algorithm, reduce the search blindness and improve the convergence speed. In addition, the global optimal location is perturbed with different sizes in different iterative stages, which effectively improves the probability of jumping out of the local optimal and ensures the balance between the global search ability and the local search ability of the algorithm. In order to evaluate the effectiveness of MSD-CSA algorithm, it is applied to 20 basic test functions for optimization experiments, and compared with other intelligent optimization algorithms. Experimental results show that the average convergence and robustness of the proposed algorithm are better than other algorithms, and the overall performance is good.