TY - GEN
T1 - A new multiple traveling salesman problem and its genetic algorithm-based solution
AU - Li, Jun
AU - Sun, Qirui
AU - Zhou, Meng Chu
AU - Dai, Xianzhong
PY - 2013
Y1 - 2013
N2 - This work formulates for the first time a multiple traveling salesman problem (MTSP) with ordinary and exclusive cities, denoted by MTSP* for short. In the original MTSP, a city can be visited by any traveling salesman and is thus renamed as an ordinary one in MTSP*. A new class of cities is introduced in MTSP*, called exclusive ones. They are divided into groups, each of which can be exclusively visited by a specified or predetermined salesman. To solve MTSP*, a genetic algorithm is presented. It encodes cities and salesman into two single chromosomes. Accordingly, three modes of crossover and mutation operators are designed, i.e., simple city crossover and mutation (CCM), simple salesman crossover and mutation, and mixed city-salesman crossover and mutation. All the operations of crossover and mutation follow the proper relationship between cities and salesman. With the help of an MTSP* example, the performance of the proposed algorithm with three modes of crossover and mutation operators is compared and analyzed. The simulation results show that the algorithm can solve MTSP* with rapid convergence with CCM being the best mode of the operators.
AB - This work formulates for the first time a multiple traveling salesman problem (MTSP) with ordinary and exclusive cities, denoted by MTSP* for short. In the original MTSP, a city can be visited by any traveling salesman and is thus renamed as an ordinary one in MTSP*. A new class of cities is introduced in MTSP*, called exclusive ones. They are divided into groups, each of which can be exclusively visited by a specified or predetermined salesman. To solve MTSP*, a genetic algorithm is presented. It encodes cities and salesman into two single chromosomes. Accordingly, three modes of crossover and mutation operators are designed, i.e., simple city crossover and mutation (CCM), simple salesman crossover and mutation, and mixed city-salesman crossover and mutation. All the operations of crossover and mutation follow the proper relationship between cities and salesman. With the help of an MTSP* example, the performance of the proposed algorithm with three modes of crossover and mutation operators is compared and analyzed. The simulation results show that the algorithm can solve MTSP* with rapid convergence with CCM being the best mode of the operators.
KW - Genetic algorithm
KW - Multiple traveling salesman problem
KW - Optimization
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U2 - 10.1109/SMC.2013.112
DO - 10.1109/SMC.2013.112
M3 - Conference contribution
AN - SCOPUS:84893570991
SN - 9780769551548
T3 - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
SP - 627
EP - 632
BT - Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
T2 - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Y2 - 13 October 2013 through 16 October 2013
ER -