Earticle

초록

영어

This paper presents a double mutation cuckoo search algorithm (DMCS) to overcome the disadvantages of traditional cuckoo search algorithms, such as bad accuracy, low convergence rate, and easiness to fall into local optimal value. The algorithm mutates optimal fitness parasitic nests using small probability, which enhances the local search range of the optimal solution and improves the search accuracy. Meanwhile, the algorithm uses large probability to mutate parasitic nests in poor situation, which enlarges the search space and benefits the global convergence. The experimental results show that the algorithms can effectively improve the convergence speed and optimization accuracy when applied to basic test functions and systems of nonlinear equations.

목차

Abstract
 1. Introduction
 2. Cuckoo Search Algorithm
 3. Double Mutation Cuckoo Search Algorithm
  3.1. Double Mutation Operator
  3.2. Initial Population Generated by a Chaotic Array
  3.3. Handling Strategy of Boundary Values
  3.4. The Procedure of the Proposed Algorithm
 4. Experiment and Results
  4.1. Algorithm Simulation and Analysis
  4.2. The Solution of the Nonlinear Equations
 5. Conclusion
 References

키워드

저자

• Chiwen Qu [ Department of Mathematics & Computer Information Engineering, Baise University, Baise 533000, China ]
• Wei He [ Department of Mathematics & Computer Information Engineering, Baise University, Baise 533000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605