Study On Cuckoo Search Algorithm For Optimization Problem

The function optimization problem is a kind of important optimization problems in our real life which is NP-hard and difficult to find a satisfying solution within a limited time. How to get the best or a satisfying solution quickly is the most interest to researchers.With the development of science and technology, more and more swarm intelligence algorithm applied to solve global optimization problems, such as particle swarm algorithm, ant colony algorithm and artificial bee colony algorithm. The results of the present study show that the swarm intelligence algorithm has great potential for solving optimization problems.A new swarm intelligence cuckoo search algorithm proposed in 2009 by Xin-she Yang and other scholars, this algorithm has been successfully applied to many practical engineering problems and design optimization problems and has obtained the very good optimization effect. But the algorithm in convergence speed, accuracy of solution, its application has the very big promotion space, for improving the convergent speed and precision of algorithm, we propose the cooperative adaptive cuckoo search algorithm and group-based cuckoo search algorithm based on cuckoo search algorithm.This paper proposes a cooperative adaptive cuckoo search algorithm(C2ACS) for improving the convergence speed of global optimization problems, the cooperative search is added to the cuckoo search algorithm, greatly increasing the speed of convergence, while adding adaptive search strategy, prevent the algorithm into a local optimum,through simulation experiments for solving function optimization problems show that this algorithm is effective,and through the experimental comparison, proved that the algorithm is better than the cooperative cuckoo search algorithm and adaptive cuckoo search algorithm.This paper proposes a group-based cuckoo search algorithm(GCS) for improving the precision of global optimization problems.Was found to be in the invasion of alien species, the birds were randomly divided into two parts of the same population size, using different random walk method to find new nest. In this way, GCS increases the diversity of the nest and optimization capability. Cuckoo search algorithm uses local search to find the nest, GCS adds a global search for seeking to increase the convergence speed. This algorithm on 23 benchmark functions were tested.Compared with some other optimization algorithms, the GCS algorithm has higher search capabilities.