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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A new MOEA for multi-objective TSP and Its convergence property analysis
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A new approach to solving dynamic traveling salesman problems
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Probabilistic based evolutionary optimizers in bi-objective travelling salesman problem
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
On designing genetic algorithms for solving small- and medium-scale traveling salesman problems
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
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Dynamic multi-objective TSP (DMOTSP), a new research filed of evolutionary computation, is an NP-hard problem which comes from the applications of mobile computing, mobile communications. Currently, only a small number of literatures related to the research of static multi-objective TSP and dynamic single objective TSP. In this paper, an evaluation criterion of the algorithms for DMOTSP called Paretos-Similarity is first proposed, with which can evaluate the Pareto set and algorithms' performance for DMOTSP. A dynamic multi-objective evolutionary algorithm for DMOTSP, DMOTSP-EA, is also proposed, which embraces an effective operator, Inver-Over, for static TSP and dynamic elastic operators for dynamic TSP. It can track the Pareto front of medium-scale dynamic multi-objective TSP in which the number of cities is between 100 and 200. In experiment, taking CHN144+5 with two objectives for example, the algorithm is tested effective and the evaluation criterion, Paretos-Similarity, is available.