AN EFFECTIVE GENETIC ALGORITHM FOR SOLVING THE MULTIPLE TRAVELING SALESMAN PROBLEM

The multiple traveling salesman problem (MTSP) involves scheduling m > 1 salesmen to visit a set of n > m nodes so that each node is visited exactly once. The objective is to minimize the total distance traveled by all the salesmen. The MTSP is an example of combinatorial optimization problems, and has a multiplicity of applications, mostly in the areas of routing and scheduling. In this paper, a modified hybrid metaheuristic algorithm called GA2OPT for solving the MTSP is proposed. In this algorithm, at the first stage, the MTSP is solved by the modified genetic Algorithm (GA) in each iteration, and, at the second stage, the 2-Opt local search algorithm is used for improving solutions for that iteration. The proposed algorithm was tested on a set of 6 benchmark instances from the TSPLIB and in all but four instances the best known solution was improved. For the rest instances, the quality of the produced solution deviates less than 0.01% from the best known solutions ever.

[1]  M. Sol The general pickup and delivery problem , 2010 .

[2]  Pan Junjie,et al.  An Ant Colony Optimization Algorithm for Multiple Travelling Salesman Problem , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[3]  Christian Artigues,et al.  A Memetic Algorithm with a large neighborhood crossover operator for the Generalized Traveling Salesman Problem , 2008, Comput. Oper. Res..

[4]  Nicos Christofides,et al.  An Algorithm for the Vehicle-dispatching Problem , 1969 .

[5]  Gregory Gutin,et al.  Lin-Kernighan heuristic adaptations for the generalized traveling salesman problem , 2010, Eur. J. Oper. Res..

[6]  Cliff T. Ragsdale,et al.  A new approach to solving the multiple traveling salesperson problem using genetic algorithms , 2006, Eur. J. Oper. Res..

[7]  Swaroop Darbha,et al.  A Lagrangian-Based Algorithm for a Multiple Depot, Multiple Travelling Salesmen Problem , 2007, ACC.

[8]  D. Kaur,et al.  Performance enhancement in solving Traveling Salesman Problem using hybrid genetic algorithm , 2008, NAFIPS 2008 - 2008 Annual Meeting of the North American Fuzzy Information Processing Society.

[9]  Lixin Tang,et al.  A multiple traveling salesman problem model for hot rolling scheduling in Shanghai Baoshan Iron & Steel Complex , 2000, Eur. J. Oper. Res..

[10]  K. Kannan,et al.  Randomized gravitational emulation search algorithm for symmetric traveling salesman problem , 2007, Appl. Math. Comput..

[11]  C. S. Orloff Routing a fleet of M vehicles to/from a central facility , 1974, Networks.

[12]  Nikbakhsh Javadian,et al.  An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems , 2011, Appl. Soft Comput..

[13]  Mauro Dell'Amico,et al.  Branch-and-cut for the pickup and delivery traveling salesman problem with FIFO loading , 2010, Comput. Oper. Res..

[14]  G A McCoy,et al.  Computer-assisted school bus routing and scheduling optimization. An evaluation of potential fuel savings and implementation alternatives , 1985 .

[15]  Fred W. Glover,et al.  Future paths for integer programming and links to artificial intelligence , 1986, Comput. Oper. Res..

[16]  Jun Zhang,et al.  A novel discrete particle swarm optimization to solve traveling salesman problem , 2007, 2007 IEEE Congress on Evolutionary Computation.

[17]  T. Bektaş The multiple traveling salesman problem: an overview of formulations and solution procedures , 2006 .

[18]  Joshua D. Knowles,et al.  Local search for the probabilistic traveling salesman problem: Correction to the 2-p-opt and 1-shift algorithms , 2005, Eur. J. Oper. Res..

[19]  W. A. Malik,et al.  A Lagrangian-Based Algorithm for a Multiple Depot, Multiple Travelling Salesmen Problem , 2007, 2007 American Control Conference.

[20]  Yanchun Liang,et al.  Particle swarm optimization-based algorithms for TSP and generalized TSP , 2007, Inf. Process. Lett..

[21]  C. Reeves Modern heuristic techniques for combinatorial problems , 1993 .