A Comprehensive Overview of the Applications of Artificial Life
Artificial Life
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
Proceedings of the 40th Conference on Winter Simulation
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Generation of pairwise test sets using a simulated bee colony algorithm
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
International Journal of Bio-Inspired Computation
Bee colony optimization for the p-center problem
Computers and Operations Research
Bee colony intelligence in zone constrained two-sided assembly line balancing problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Empirical study of the Bee Colony Optimization (BCO) algorithm
Expert Systems with Applications: An International Journal
Transit network design by Bee Colony Optimization
Expert Systems with Applications: An International Journal
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Ant colony optimisation for vehicle traffic systems: applications and challenges
International Journal of Bio-Inspired Computation
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Artificial Life (ALife) uses biological knowledge and techniques to help solve different engineering, management, control and computational problems. Natural systems teach us that very simple individual organisms can form systems capable of performing highly complex tasks by dynamically interacting with each other. The main goal of this paper is to show how we can use ALife concepts (inspired by some principles of natural swarm intelligence) when solving complex problems in traffic and transportation. The Bee System that represents the new approach in the field of Swarm Intelligence is described. It is also shown in the paper that ALife approach can be successful to "attack" transportation problems characterized byuncertainty. The Fuzzy Ant System (FAS) described in the paper represents an attempt to handle the uncertainty that sometimes exists in some complex transportation problems. The potential applications of the Bee System and the Fuzzy Ant System in the field of Traffic and Transportation Engineering are discussed.