The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Kangping Wang - One of the best experts on this subject based on the ideXlab platform.
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particle swarm optimization for traveling salesman problem
International Conference on Machine Learning and Cybernetics, 2003Co-Authors: Kangping Wang, La Huang, Chunguang Zhou, Wei PangAbstract:This paper proposes a new application of particle swarm optimization for traveling salesman problem. We have developed some special methods for solving TSP using PSO. We have also proposed the concept of swap operator and swap sequence, and redefined some operators on the basis of them, in this way the paper has designed a special PSO. The experiments show that it can achieve good results.
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hybrid ant colony algorithm for traveling salesman problem
Progress in Natural Science, 2003Co-Authors: Lan Huang, Chunguang Zhou, Kangping WangAbstract:Abstract A hybrid approach based on ant colony algorithm for the traveling salesman problem is proposed, which is an improved algorithm characterized by adding a local search mechanism, a cross-removing strategy and candidate lists. Experimental results show that it is competitive in terms of solution quality and computation time.
Quanqe Pan - One of the best experts on this subject based on the ideXlab platform.
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a discrete particle swarm optimization algorithm for the generalized traveling salesman problem
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Mehmet Fatih Tasgetiren, P N Suganthan, Quanqe PanAbstract:Dividing the set of nodes into clusters in the well-known traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speed-up methods for greedy node insertions are presented. The discrete particle swarm optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve the generalized traveling salesman problem.
Mehmet Fatih Tasgetiren - One of the best experts on this subject based on the ideXlab platform.
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a discrete particle swarm optimization algorithm for the generalized traveling salesman problem
Genetic and Evolutionary Computation Conference, 2007Co-Authors: Mehmet Fatih Tasgetiren, P N Suganthan, Quanqe PanAbstract:Dividing the set of nodes into clusters in the well-known traveling salesman problem results in the generalized traveling salesman problem which seeking a tour with minimum cost passing through only a single node from each cluster. In this paper, a discrete particle swarm optimization is presented to solve the problem on a set of benchmark instances. The discrete particle swarm optimization algorithm exploits the basic features of its continuous counterpart. It is also hybridized with a local search, variable neighborhood descend algorithm, to further improve the solution quality. In addition, some speed-up methods for greedy node insertions are presented. The discrete particle swarm optimization algorithm is tested on a set of benchmark instances with symmetric distances up to 442 nodes from the literature. Computational results show that the discrete particle optimization algorithm is very promising to solve the generalized traveling salesman problem.
Wei Ying-hui - One of the best experts on this subject based on the ideXlab platform.
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Study on multiple traveling salesman problem based on genetic algorithm
Journal of Computer Applications, 2009Co-Authors: Wei Ying-huiAbstract:traveling salesman problem is a classical complete nondeterministic polynomial problem. It is significant to solve Multiple traveling salesman problems (MTSP). Previous researches on multiple traveling salesman problem are mostly limited to the kind that employed total-path-shortest as the evaluating rule, but little notice is made on the kind that employed longest-path-shortest as the evaluating rule. In order to solve this problem, genetic algorithm was used to optimize it and decoding method with matrix was proposed. It is fit for solving symmetric and asymmetric MTSP. Symmetric and asymmetric multiple traveling salesman problems were simulated and different crossover operators were compared.
Barrett W Thomas - One of the best experts on this subject based on the ideXlab platform.
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probabilistic traveling salesman problem with deadlines
Transportation Science, 2008Co-Authors: Ann Melissa Campbell, Barrett W ThomasAbstract:Time-constrained deliveries are one of the fastest growing segments of the delivery business, and yet there is surprisingly little literature that addresses time constraints in the context of stochastic customer presence. We begin to fill that void by introducing the probabilistic traveling salesman problem with deadlines (PTSPD). The PTSPD is an extension of the well-known probabilistic traveling salesman problem (PTSP) in which, in addition to stochastic presence, customers must also be visited before a known deadline. We present two recourse models and a chance constrained model for the PTSPD. Special cases are discussed for each model, and computational experiments are used to illustrate under what conditions stochastic and deterministic models lead to different solutions.
