Discrete swallow swarm optimization algorithm for travelling salesman problem

Swallow Swarm Optimization is a new metaheuristic of swarm intelligence based algorithm appeared by Neshat in 2013 in the continuous case. This optimization algorithm inspired by the intelligent behaviors of swallows. In This paper, we provide an adaptation of the swallow swarm optimization (SSO) to solve the famous traveling salesman problem (TSP), as one of the known combinatorial optimization problems. In order to test the performance of the algorithm described herein, we resolve a set of benchmark instances from TSPLIB library. The results obtained demonstrate that DSSO is performant than other metaheuristics methods.

[1]  John Fulcher,et al.  Computational Intelligence: An Introduction , 2008, Computational Intelligence: A Compendium.

[2]  Mohammed Essaid Riffi,et al.  A novel hybrid penguins search optimization algorithm to solve travelling salesman problem , 2015, 2015 Third World Conference on Complex Systems (WCCS).

[3]  Li-Pei Wong,et al.  A Bee Colony Optimization Algorithm for Traveling Salesman Problem , 2008, 2008 Second Asia International Conference on Modelling & Simulation (AMS).

[4]  T. Yasuda,et al.  Autonomous Task Allocation for Swarm Robotic Systems Using Behavioral Decomposition , 2017 .

[5]  M. E. Riffi,et al.  ADAPTATION OF THE HARMONY SEARCH ALGORITHM TO SOLVE THE TRAVELLING SALESMAN PROBLEM , 2014 .

[6]  Mohammed Essaid Riffi,et al.  A novel discrete bat algorithm for solving the travelling salesman problem , 2015, Neural Computing and Applications.

[7]  Kevin M. Passino,et al.  Bacterial Foraging Optimization , 2010, Int. J. Swarm Intell. Res..

[8]  Jens Vygen,et al.  The Book Review Column1 , 2020, SIGACT News.

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

[10]  Athanasios Migdalas,et al.  A hybrid Particle Swarm Optimization - Variable Neighborhood Search algorithm for Constrained Shortest Path problems , 2017, Eur. J. Oper. Res..

[11]  Palvinder Singh Mann,et al.  Energy-Efficient Hierarchical Routing for Wireless Sensor Networks: A Swarm Intelligence Approach , 2017, Wirel. Pers. Commun..

[12]  K. Katayama,et al.  The efficiency of hybrid mutation genetic algorithm for the travelling salesman problem , 2000 .

[13]  Pei-wei Tsai,et al.  Cat Swarm Optimization , 2006, PRICAI.

[14]  Marco Dorigo,et al.  Ant colony optimization , 2006, IEEE Computational Intelligence Magazine.

[15]  MOHAMMED ESSAID RIFFI DISCRETE PENGUINS SEARCH OPTIMIZATION ALGORITHM TO SOLVE THE TRAVELING SALESMAN PROBLEM , 2015 .

[16]  N. Biggs THE TRAVELING SALESMAN PROBLEM A Guided Tour of Combinatorial Optimization , 1986 .

[17]  Christopher C. Skiscim,et al.  Optimization by simulated annealing: A preliminary computational study for the TSP , 1983, WSC '83.

[18]  Hrvoje Gold,et al.  Vehicle Routing Problem , 2008, Encyclopedia of GIS.

[19]  Sumanta Basu Tabu Search Implementation on Traveling Salesman Problem and Its Variations: A Literature Survey , 2012 .

[20]  Mohammed Essaid Riffi,et al.  Discrete Cat Swarm Optimization to Resolve the Traveling Salesman Problem , 2013 .

[21]  Debasish Ghose,et al.  Glowworm Swarm Optimization for Searching Higher Dimensional Spaces , 2009, Innovations in Swarm Intelligence.

[22]  Yuichi Nagata,et al.  A new genetic algorithm for the asymmetric traveling salesman problem , 2012, Expert Syst. Appl..

[23]  Suyanto,et al.  Evolutionary Discrete Firefly Algorithm for Travelling Salesman Problem , 2011, ICAIS.

[24]  Maurice Clerc,et al.  Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem , 2004 .

[25]  Saeed Bagheri Shouraki,et al.  An artificial immune system with partially specified antibodies , 2007, GECCO '07.