Earticle

초록

영어

In order to improve the slow convergence speed and avoid falling into the local optimum in ant colony optimization algorithm, an improved quantum ant colony optimization (IMAQACO) algorithm based on combing quantum evolutionary algorithm with ant colony optimization algorithm is proposed for solving complex function problems in this paper. In the IMAQACO algorithm, the quantum state vectors are used to represent the pheromone, the adaptively dynamical updating strategy is used to control pheromone evaporation factor, the quantum rotation gate is used to realize the ant movement and change the convergence tend of quantum probability amplitude, quantum non-gate is used to realize ant location variation, so the IMAQACO algorithm has better global search ability and population diversity than ACO algorithm. In order to test the optimization performance of IMAQACO algorithm, several benchmark functions are selected in here. The tested results indicate that the IMAQACO can effectively improve the convergence speed and avoid falling into the local optimum, and has a stronger global optimization ability and higher convergence speed in solving complex function problems.

목차

Abstract
 1. Introduction
 2. ACO Algorithm
 3. Quantum Evolutionary Algorithm (QEA)
 4. An Improved Quantum Ant Colony Optimization (IMAQACO) Algorithm
  4.1. Adaptive Adjustment Strategy of Quantum Rotation Gate
  4.2.The Position Update of Ant
  4.3. The Position Mutation of Ant
  4.4. The Flow Description of IMAQACO Algorithm
 5. Experiment Results and Analysis
 6. Conclusion
 References

키워드

저자

• Changai Chen [ Department of Information Technology, Henan University of TCM, Zhengzhou, Henan 450008 China ]
• Yanwen Xu [ Department of Information Technology, Henan University of TCM, Zhengzhou, Henan 450008 China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605