Adaptive cuckoo algorithm with multiple search strategies

Metaheuristic algorithms are important methods to solve optimization problems and maintaining a balance between the global exploration and local exploitation is crucial to the performance of such algorithms. We propose a self-adaptive multi strategy cuckoo search algorithm (MSACS) based on the cuckoo search algorithm (CS). First, five different search strategies were proposed to calculate the use probability and control parameters by using adaptive strategies to ensure that the algorithm can autonomously adjust according to the change in the functions and iteration times. Second, the performance of the MSACS was tested on 28 common benchmark functions and compared with the performance of several CS algorithms, particle swarm optimization (PSO) algorithms and difference evolution algorithms (DE). MSACS achieved the best results on 17 of these functions and performed well on the remaining 11 functions. Finally, the improved algorithm was applied to the optimization of a ball screw driving system model. By adjusting the dimensionless input velocity function, the peak acceleration of screw is reduced and the peak acceleration of crank angle is reasonable.