Ant algorithms for discrete optimization
Artificial Life
A Graph-based Ant system and its convergence
Future Generation Computer Systems
Future Generation Computer Systems
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Ant Colony Optimization
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
A proof of convergence for Ant algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Ant colony optimization theory: a survey
Theoretical Computer Science
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
How mutation and selection solve long-path problems in polynomial expected time
Evolutionary Computation
Rigorous hitting times for binary mutations
Evolutionary Computation
The Improved Ant Colony Algorithm Based on Immunity System Genetic Algorithm and Application
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
A new approach to estimating the expected first hitting time of evolutionary algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A time complexity analysis of ACO for linear functions
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
Runtime analysis of a simple ant colony optimization algorithm
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Cognitive informatics models of the brain
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Statistical distribution of the convergence time of evolutionaryalgorithms for long-path problems
IEEE Transactions on Evolutionary Computation
A short convergence proof for a class of ant colony optimizationalgorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A Runtime Analysis of Evolutionary Algorithms for Constrained Optimization Problems
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Recourse-based facility-location problems in hybrid uncertain environment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
MSDP with ACO: A maximal SRLG disjoint routing algorithm based on ant colony optimization
Journal of Network and Computer Applications
A method for avoiding the searching bias in ACO deceptive problem solving
Web Intelligence and Agent Systems
Ant colony optimisation for vehicle traffic systems: applications and challenges
International Journal of Bio-Inspired Computation
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Ant colony optimization (ACO) has widely been applied to solve combinatorial optimization problems in recent years. There are few studies, however, on its convergence time, which reflects how many iteration times ACO algorithms spend in converging to the optimal solution. Based on the absorbing Markov chain model, we analyze the ACO convergence time in this paper. First, we present a general result for the estimation of convergence time to reveal the relationship between convergence time and pheromone rate. This general result is then extended to a two-step analysis of the convergence time, which includes the following: 1) the iteration time that the pheromone rate spends on reaching the objective value and 2) the convergence time that is calculated with the objective pheromone rate in expectation. Furthermore, four brief ACO algorithms are investigated by using the proposed theoretical results as case studies. Finally, the conclusions of the case studies that the pheromone rate and its deviation determine the expected convergence time are numerically verified with the experiment results of four one-ant ACO algorithms and four ten-ant ACO algorithms.