Discrete ITÔ algorithm to the coloured travelling salesman problem

The Coloured Travelling Salesman Problem CTSP, a generalised version of the Multiple Travelling Salesman Problem MTSP, has been proposed to model some real-world applications. This work proposes a discrete ITO DITO algorithm to solve CTSP. It combines the continuous ITO stochastic process with Ant Colony Optimisation ACO algorithm. First, the stochastic drift and volatility terms of ITO process are designed to be suitable for solving combinatorial optimisation problems, such as TSP and CTSP. And then, inspired by ACO, the generative model for a feasible solution of CTSP is constructed via the distance between cities and the experience gathered in the searching process. The experiments results show that the performance of DITO is superior to some state-of-the-art meta-heuristic algorithms in terms of the quality of the solution and computational time.

[1]  Bezalel Gavish,et al.  An Optimal Solution Method for Large-Scale Multiple Traveling Salesmen Problems , 1986, Oper. Res..

[2]  Keld Helsgaun,et al.  General k-opt submoves for the Lin–Kernighan TSP heuristic , 2009, Math. Program. Comput..

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

[4]  J. Desrosiers,et al.  Lagrangian relaxation methods for solving the minimum fleet size multiple traveling salesman problem with time windows , 1988 .

[5]  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..

[6]  Bruce L. Golden,et al.  Solving vehicle routing problems using elastic nets , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[7]  William A. Gruver,et al.  Team scheduling by genetic search , 1999, Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials. IPMM'99 (Cat. No.99EX296).

[8]  Gang Xu,et al.  Premature convergence of standard particle swarm optimisation algorithm based on Markov chain analysis , 2015, Int. J. Wirel. Mob. Comput..

[9]  Kay Chen Tan,et al.  A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the Multiobjective Multiple Traveling Salesman Problem , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Lijin Wang,et al.  One-position inheritance based cuckoo search algorithm , 2015, Int. J. Comput. Sci. Math..

[11]  Zhang Yi,et al.  A columnar competitive model for solving multi-traveling salesman problem☆ , 2007 .

[12]  Ting-gui Chen,et al.  Comparative analysis of swarm intelligence and heuristic priority rules for solving multi-project scheduling problem , 2015, Int. J. Comput. Sci. Math..

[13]  Dong Wen Convergence and Runtime Analysis of ITO Algorithm for One Class of Combinatorial Optimization , 2011 .

[14]  MengChu Zhou,et al.  Colored Traveling Salesman Problem , 2015, IEEE Transactions on Cybernetics.

[15]  Berthold Vöcking,et al.  Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP , 2007, SODA '07.

[16]  Yong Lu,et al.  An improved artificial bee colony with new search strategy , 2015, Int. J. Wirel. Mob. Comput..

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

[18]  Yuanxiang Li,et al.  The Multi-objective ITO Algorithms , 2007, ISICA.

[19]  Takao Enkawa,et al.  Competition-based neural network for the multiple travelling salesmen problem with minmax objective , 1999, Comput. Oper. Res..

[20]  Neha Verma,et al.  Energy-efficient sensor optimisation in wireless sensor networks , 2015, Int. J. Wirel. Mob. Comput..

[21]  Chau-Yun Hsu,et al.  A study of feature-mapped approach to the multiple travelling salesmen problem , 1991, 1991., IEEE International Sympoisum on Circuits and Systems.

[22]  Kenneth DeJong,et al.  Evolutionary Computational Approaches to Solving the Multiple Traveling Salesman Problem Using a Neighborhood Attractor Schema , 2002, EvoWorkshops.

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

[24]  Qing Yu,et al.  A hybrid artificial bee colony algorithm based on different search mechanisms , 2015, Int. J. Wirel. Mob. Comput..