TSPAntSim

An interactive simulation and analysis software for solving TSP using ant colony optimization algorithms The traveling salesman problem (TSP) is one of the extensively studied combinatorial optimization problems and tries to find the shortest route for salesperson which visits each given city precisely once. Ant colony optimization (ACO) algorithms have been used to solve many optimization problems in various fields of engineering. In this paper, a web-based simulation and analysis software (TSPAntSim) is developed for solving TSP using ACO algorithms with local search heuristics. Algorithms are tested on benchmark problems from TSPLIB and test results are presented. Importance of TSPAntSim providing also interactive visualization with real-time analysis support for researchers studying on optimization and people who have problems in form of TSP is discussed.

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 6 articles , 1 standard article )

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  1. Simons, C. L.; Smith, J. E.: A comparison of meta-heuristic search for interactive software design (2013) ioport
  2. Wang, Huawei; Luo, Yuxiao; Shi, Zhijian: Real-time gate reassignment based on flight delay feature in hub airport (2013) ioport
  3. He, Jiajia; Hou, Zaien: Ant colony algorithm for traffic signal timing optimization (2012)
  4. Król, Dariusz; Drożdżowski, Maciej: Use of MaSE methodology for designing a swarm-based multi-agent system (2010)
  5. Wang, Wei; Guo, Shijun; Chang, Nan; Zhao, Feng; Yang, Wei: A modified ant colony algorithm for the stacking sequence optimisation of a rectangular laminate (2010) ioport
  6. Uğur, Aybars; Aydin, Doğan: An interactive simulation and analysis software for solving TSP using ant colony optimization algorithms (2009)