Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
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
Memory-based immigrants for genetic algorithms in dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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
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
Ant colony optimization with immigrants schemes for the dynamic vehicle routing problem
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
A benchmark generator for dynamic permutation-encoded problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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Ant colony optimization (ACO) algorithms have proved that they can adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity. DOPs are important due to their similarities to many real-world applications. Several approaches have been integrated with ACO to improve their performance in DOPs, where memory-based approaches and immigrants schemes have shown good results on different variations of the dynamic travelling salesman problem (DTSP). In this paper, we consider a novel variation of DTSP where traffic jams occur in a cyclic pattern. This means that old environments will re-appear in the future. A hybrid method that combines memory and immigrants schemes is proposed into ACO to address this kind of DTSPs. The memory-based approach is useful to directly move the population to promising areas in the new environment by using solutions stored in the memory. The immigrants scheme is useful to maintain the diversity within the population. The experimental results based on different test cases of the DTSP show that the memory-based immigrants scheme enhances the performance of ACO in cyclic dynamic environments.