Earticle

초록

영어

In order to overcome the blindness of chaotic search, improve the convergence speed and global solving ability of the basic ant colony optimization(ACO) algorithm, an improved ACO algorithm based on combining multi-population strategy, adaptive adjustment pheromone strategy, chaotic search method and min-max ant strategy (MPCSMACO)is proposed in this paper. In the proposed MPCSMACO algorithm, the multi-population strategy is introduced to realize the information exchange and cooperation among the various types of ant colony. The chaotic search method with the ergodicity, randomness and regularity by using the logistic mapping is used to overcome too long search time, avoid falling into the local extremum in the initial stage and improve the search accuracy in the late search. The min-max ant strategy is used to avoid the local optimization solution and the stagnation. And the ants with different probability search different area according to the concentration of pheromone, so as to reduce the search number of the blindness of chaotic search method. Several Benchmark functions are selected to testify the performance of the MPCSMACO algorithm. The experiment results show that the MPCSMACO algorithm takes on the better global search ability and convergence performance.

목차

Abstract
 1. Introduction
 2. Ant Colony Optimization Algorithm
 3. Chaotic Search Method
 4. Several Strategies
  4.1. Multi-Population Strategy
  4.2. The Min-Max Ant Strategy
  4.3. Adaptive Adjustment Pheromone Strategy
 5. An Improved ACO (MPCSMACO) Algorithm
 6. Experimental Analysis
 7. Conclusion
 References

키워드

저자

• Yue Liu [ Institute of Information Engineering, Kaifeng University, Kaifeng 475004, China ]
• Xiaoting Wang [ Institute of Information Engineering, Kaifeng University, Kaifeng 475004, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605