The ant colony optimization meta-heuristic
New ideas in optimization
Ant algorithms for discrete optimization
Artificial Life
A parallel implementation of ant colony optimization
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
Parallelization Strategies for Ant Colony Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
Exchange strategies for multiple Ant Colony System
Information Sciences: an International Journal
A Bio-Inspired Approach for a Dynamic Railway Problem
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
An ant-based technique for the dynamic generalized traveling salesman problem
ISTASC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Systems Theory and Scientific Computation - Volume 7
Explicit and Emergent Cooperation Schemes for Search Algorithms
Learning and Intelligent Optimization
Multi-thread integrative cooperative optimization for rich combinatorial problems
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Parallel ant colony optimization for the traveling salesman problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. Ant Colony Optimization is a metaheurisitc that is able to solve large scale optimization problems. In the dynamic traveling salesman problem, the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their neighborhoods. The algorithm is tested with success on several large data sets. The paper concludes with a discussion of the results provided by both the sequential and parallel approaches and calls for further research on the subject.