Memory-based CHC algorithms for the dynamic traveling salesman problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
CHC-based algorithms for the dynamic traveling salesman problem
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Extended virtual loser genetic algorithm for the dynamic traveling salesman problem
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A classification scheme for agent based approaches to dynamic optimization
Artificial Intelligence Review
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This paper presents an agent-based evolutionary search algorithm (AES) for solving dynamic travelling salesman problem (DTSP). The proposed algorithm uses the principal of collaborative endeavor learning mechanism in which all the agents within the current population co-evolve to track dynamic optima. Moreover, a local updating rule which is much the same of permutation enforcement learning scheme is induced for diversity maintaining in dynamic environments. The developed search algorithm and benchmark generator are then built to test the evolutionary model for dynamic versions of travelling salesman problem. Experimental results demonstrate that the proposed method is effective on dynamic problems and have a great potential for other dynamic combinatorial optimization problems as well.