Application Research Of The Cuckoo Search Algorithm And Algorithm Performance Metrics

A new metaheuristic optimisation algorithm, called Cuckoo Search (CS), was developed recently by Yang and Deb (2009). This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with Levy flight behavior of some birds and fruit flies.This paper presents a more extensive comparison study using some standard test functions and newly designed stochastic test functions.The optimal solutions obtained by CS are far better than the best solutions obtained by an efficient particle swarm optimiser and genetic algorithms. CS is a population-based algorithm, the randomization is more efficient as the step length is heavy-tailed, and any large step is possible. The number of parameters to be tuned is less than GA and PSO, and thus it is potentially more generic to adapt to a wider class of optimization problems. CS was drawn the attention of both the domestic and international scholars.This paper main improve the CS. Then, we will apply it to solve design optimization problems in engineering. We give a method for measuring its performance.The main results of this study are as follows:First, analyze the operation mode and update formula of CS,then improve it.In this paper,We put forward an improved algorithm of cuckoo search(MCS). Finally,the MCS improve the rate of convergence of CS.Second, combine Penalty function method and the improved Cuckoo algorithm,then we put forward a new algorithms,called PMCS.And,we aplly it to solve design optimization problems in engineering. It turns out that the rough simulation based PMCS algorithm is efficient than Genetic Algorithms.Last, by defining limit inferior of a sequence space of sets,and in this paper a general evaluation method of algorithm performance is proposed. After analyzing their performances by comparing the simulation results, we can conclude that method reflets the difference of algorithm.It is proved to be of good commonality.