Title :
Convergence Analysis of a Class of Adaptive Ant Colony Algorithm
Author :
Zhao, Baojiang ; Li, Shiyong
Author_Institution :
Dept. of Control Sci. & Eng., Harbin Inst. of Technol.
Abstract :
This paper presents a class of adaptive ant colony optimization algorithm and proves its convergence properties. The global searching and convergence ability are improved by adaptively changing the pheromone trails evaporation factors and decreasing lower pheromone bound. Markov process analysis is used to prove convergence properties of the algorithms. It is shown that its current solutions of the system converge, with probability one, to an optimal solution of the system
Keywords :
Markov processes; artificial life; convergence of numerical methods; optimisation; Markov process analysis; adaptive ant colony optimization; convergence analysis; global searching; pheromone trails evaporation factors; Algorithm design and analysis; Ant colony optimization; Convergence; Educational institutions; Markov processes; Mathematics; Simulated annealing; Stochastic processes; Ant colony optimization; convergence; markov process;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1713024
