A proof of convergence for Ant algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Joint optimization for area traffic control and network flow
Computers and Operations Research
Ant colony optimization for the traveling purchaser problem
Computers and Operations Research
An iterative approach to enhanced traffic signal optimization
Expert Systems with Applications: An International Journal
Advances in Engineering Software
An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup
Computers and Operations Research
A hybrid ant colony optimization technique for power signal pattern classification
Expert Systems with Applications: An International Journal
Computers and Operations Research
TSOIA: An efficient node selection algorithm facing the uncertain process for Internet of Things
Journal of Network and Computer Applications
A method for avoiding the searching bias in ACO deceptive problem solving
Web Intelligence and Agent Systems
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In order to separate the conflict of the traffic flow effectively, time delay, number of stops and traffic capacity are chosen as performance indexes, and the objective function related to the cycle time and the saturation of an intersection is established by using the weighting coefficients. Then, based on the uncertainty and convergence analysis of ant colony algorithm (ACA), computational experiments are conducted and numerical comparisons are made for the values of performance indexes achieved by the signal timing optimization problem with Webster algorithm, genetic algorithm (GA) and ACA. Numerical results show that ACA is a simple and feasible method for signal timing optimization problems.