Using Movement Range Strategy of the Improved Ant Colony Algorithm for Solving Continuous Problems

Abstract:

The discrete nature of ant colony algorithm (ACO) and the characteristics of the distributed parallel computation and positive feedback had been made it widely used in discrete space problems, but it limited its application in continuous problems and now studies was relatively few. Articles improved the basic ACO to solve problems in continuous domain. The algorithm improved the way of pheromone to keep, update and advance, and limited it in a Max-Min interval at the same time, avoiding the stagnation and restricted diffusion of the algorithm, enhanced the performance of convergence. Simulation example proves that the improved ACO can quickly find good global solution on the continuum.

• Periodical: Applied Mechanics and Materials (Volumes 635-637)
• Pages: 1755-1759
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.635-637.1755
• Online since: September 2014
• Authors: Kang Huang, Zhan Yu Wu*, Guan Hong Wu
• Keywords: ACO, Continuous Domain, Pheromone

References

[1] Duan Haibin. The principle of ant colony algorithm and its application [M]. Beijing: Science Press, 2005. 12.

[2] Stützle T, Hoos H. MAX-MIN ant system and local search for the traveling salesman problem [C]. Proceedings of the 1997 IEEE International Conference on Evolutionary Computation, Indianapolis, 1997: 309-314.

DOI: 10.1109/icec.1997.592327

[3] Wang Ying, Xie Jianying. An adaptive ant colony algorithm and its simulation research [J]. Journal of System Simulation, 2002, 14(1): 31-33.

[4] Duan Haibin, Ma Guanjun, Wang Daobo, etc. An improved ant colony algorithm to solve the continuous space optimization problem [J]. Journal of System Simulation, 2007, 19(5): 974-977.