Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.

Jie-Sheng Wang,Shu-Xia Li,Jiang-Di Song

doi:10.1155/2015/374873

Abstract

In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

Highlights

Cuckoo search (CS) algorithm, a new biological heuristic algorithm, is put forward by Yang and Deb in 2009
In order to make up the defect of the algorithm on this aspect, a kind of improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed
In order to verify the performances of the improved CS algorithm, six typical continuous test functions are chosen for carrying out the simulation research, which is compare simulation results with artificial bee colony (ABC), particle swarm optimization (PSO), CS and GCS

Summary

Introduction

Cuckoo search (CS) algorithm, a new biological heuristic algorithm, is put forward by Yang and Deb in 2009 It simulates the cuckoo’s seeking nest and spawning behavior and introduces Levy flight mechanism into it, which is able to quickly and efficiently find the optimal solution [1, 2]. In order to make up the defect of the algorithm on this aspect, a kind of improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. The six typical test functions are chosen for simulation experiment and the simulation results proved that the improved cuckoo search algorithm has better convergence rate and optimization accuracy.

Cuckoo Search Algorithm

The Improved Cuckoo Search Algorithm

Simulation Results and Analysis

Conclusion