Research And Application Of Cuckoo Search Algorithm

Intelligent algorithms are problem solving algorithms inspired by natural phenomena or biological social behavior,also known as heuristic algorithms.Intelligent algorithm can search the optimal solution in the direction guided by the adaptive value function according to its own random search strategy in the solution space.Compared with the traditional algorithm,it has more advantages in solving complex problems,and has been widely applied in many fields,attracting great attention.Swarm intelligence algorithm is a kind of intelligent algorithm,which takes the social behavior of biological groups as the bionic principle and has good performance in solving specific practical problems.Cuckoo search algorithm is a new swarm intelligence algorithm.Cuckoo search algorithm has attracted much attention due to its advantages of simple structure,few parameters and good operability,which provides a feasible method for solving many optimization problems.In this paper,cuckoo search algorithm is selected as the research object,related research and application of cuckoo are discussed,and further research and application of cuckoo search algorithm itself is carried out,specifically,there are the following points:(1)The basic cuckoo algorithm principle and algorithm research are analyzed and summarized.The basic parameters involved in cuckoo algorithm are analyzed one by one,and the research and application of the algorithm are summarized,the principle,characteristics,research trend and application level of the algorithm are clarified,and the algorithm is comprehensively expounded.(2)The basic cuckoo algorithm is proposed to improve both the step size and the discovery probability to balance the global search and local search capabilities of the algorithm.The cuckoo algorithm based on the adaptive step size and the cuckoo algorithm based on the adaptive step size and the discovery probability are proposed.Eight test functions are selected for the test,and the PID parameter tuning problem is used for an example,which is compared with other intelligent algorithms.The test shows that the improved method improves the convergence and search efficiency of the algorithm,and improves the performance of the algorithm.(3)The improved cuckoo algorithm combined with the optimal perturbation strategy is applied to optimize the K-means clustering algorithm.Aiming at the problem that K-means algorithm is easy to fall into local optimal solution and cuckoo algorithm has blindness in searching solution.The improved cuckoo algorithm and the optimal perturbation strategy are used to optimize the K-means algorithm.After experimental tests on the effectiveness andconvergence of the algorithm.The improved cuckoo algorithm applied to optimize K-means clustering algorithm has a good effect.