GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.

[1]  Nik Bessis,et al.  An Autonomic Agent Trust Model for IoT systems , 2013, EUSPN/ICTH.

[2]  Daniel Gutiérrez-Reina,et al.  Multi-objective performance optimization of a probabilistic similarity/dissimilarity-based broadcasting scheme for mobile ad hoc networks in disaster response scenarios , 2013, Soft Computing.

[3]  Xuanjing Shen,et al.  An adaptive ant colony algorithm based on common information for solving the Traveling Salesman Problem , 2012, 2012 International Conference on Systems and Informatics (ICSAI2012).

[4]  LI Zhi-yon Discrete bat algorithm for solving minimum ratio traveling salesman problem , 2015 .

[5]  Pierre T. Kabamba,et al.  Stability of Solutions to Classes of Traveling Salesman Problems , 2016, IEEE Transactions on Cybernetics.

[6]  Hui Cheng,et al.  Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks , 2009, 2009 IEEE Congress on Evolutionary Computation.

[7]  Yong Wang A Nearest Neighbor Method with a Frequency Graph for Traveling Salesman Problem , 2014, 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics.

[8]  William J. Cook,et al.  In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation , 2011 .

[9]  T. Stützle,et al.  MAX-MIN Ant System and local search for the traveling salesman problem , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[10]  Aimin Zhou,et al.  Benchmarking algorithms for dynamic travelling salesman problems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[11]  Monia Bellalouna,et al.  A branch and bound algorithm for the porbabilistic traveling salesman problem , 2015, 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[12]  Urszula Boryczka,et al.  A Hybrid Discrete Particle Swarm Optimization with Pheromone for Dynamic Traveling Salesman Problem , 2012, ICCCI.

[13]  Jürgen Branke,et al.  Multi-swarm Optimization in Dynamic Environments , 2004, EvoWorkshops.

[14]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[15]  Ming Yang,et al.  A New Approach to Solving Dynamic Traveling Salesman Problems , 2006, SEAL.

[16]  Raulcezar M. F. Alves,et al.  Using genetic algorithms to minimize the distance and balance the routes for the multiple Traveling Salesman Problem , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[17]  Valentin Cristea,et al.  Optimizing the Energy Efficiency of Message Exchanging for Service Distribution in Interoperable Infrastructures , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.

[18]  Michael Guntsch,et al.  Applying Population Based ACO to Dynamic Optimization Problems , 2002, Ant Algorithms.

[19]  Gaurav Singh,et al.  Implementation of Travelling Salesman Problem Using ant Colony Optimization , 2014 .

[20]  Isa Maleki,et al.  New Approach for Solving Dynamic Traveling Salesman Problem with Hybrid Genetic Algorithms and Ant Colony Optimization , 2012 .

[21]  Ivan Stojmenovic,et al.  The one-commodity traveling salesman problem with selective pickup and delivery: An ant colony approach , 2010, IEEE Congress on Evolutionary Computation.

[22]  Avni Rexhepi,et al.  Solving TSP using Genetic Algorithms-Case of Kosovo , 2012 .

[23]  Shengxiang Yang,et al.  Ant Colony Optimization Algorithms with Immigrants Schemes for the Dynamic Travelling Salesman Problem , 2013 .

[24]  L. Darrell Whitley,et al.  Use of explicit memory in the dynamic traveling salesman problem , 2014, GECCO.

[26]  Shengxiang Yang,et al.  Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems , 2017, IEEE Transactions on Cybernetics.

[27]  T. Neumann Computers And Intractability A Guide To The Theory Of Np Completeness , 2016 .

[28]  Patricia A. Owen,et al.  Decisions that influence outcomes in the distant future , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[29]  J. Lenstra,et al.  In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation , 2016 .

[30]  Shoudong Huang,et al.  A new crossover approach for solving the multiple travelling salesmen problem using genetic algorithms , 2013, Eur. J. Oper. Res..

[31]  Ernesto Costa,et al.  Multi-caste Ant Colony Algorithm for the Dynamic Traveling Salesperson Problem , 2013, ICANNGA.