Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Pheromone Modification Strategies for Ant Algorithms Applied to Dynamic TSP
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Inver-over Operator for the TSP
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
Applying Population Based ACO to Dynamic Optimization Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Ant Colony Optimization
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
Ant colony optimization with immigrants schemes in dynamic environments
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Memory-based immigrants for ant colony optimization in changing environments
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adapting the pheromone evaporation rate in dynamic routing problems
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity and transfer knowledge. Several approaches have been integrated with ACO to improve its performance for DOPs. Among these integrations, the ACO algorithm with immigrants schemes has shown good results on the dynamic travelling salesman problem. In this paper, we investigate ACO algorithms to solve a more realistic DOP, the dynamic vehicle routing problem (DVRP) with traffic factors. Random immigrants and elitism-based immigrants are applied to ACO algorithms, which are then investigated on different DVRP test cases. The results show that the proposed ACO algorithms achieve promising results, especially when elitism-based immigrants are used.