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
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
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
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Ant colony optimization (ACO) algorithms have proved to be powerful methods to address dynamic optimization problems. However, once the population converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to the new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution is to maintain the diversity via transferring knowledge to the pheromone trails. Hence, we propose an immigrants scheme based on environmental information for ACO to address the dynamic travelling salesman problem (DTSP) with traffic factor. The immigrants are generated using a probabilistic distribution based on the frequency of cities, constructed from a number of ants of the previous iteration, and replace the worst ants in the current population. Experimental results based on different DTSP test cases show that the proposed immigrants scheme enhances the performance of ACO by the knowledge transferred from the previous environment and the generation of guided diversity.