Abstract
Self-organizing map (SOM) is a powerful variant of neural network for solving optimization problems. Many researchers have reported SOM for Traveling Salesperson Problem; however, problems still exist due to the trapping of the optimization techniques at the local optimal position. In this work, we propose an Extended Self-Organizing Map based on 2-opt algorithm with one-dimensional neighborhood to approach the Symmetrical Traveling Salesperson Problem (STSP). We elaborate our approach for STSP where weights of neurons represent nodes that are placed in the polygonal domain. The selection of winner neuron of SOM has been extended to overcome the problem of trapping of SOM at local optima. The results of SOM are improved through 2-opt local optimization algorithm. We briefly discuss self-organization in neural networks, 2-opt algorithm, and extension applied to SOM. Finally, the algorithm is compared with Kohonen Self-Organizing Map and Evolutionary Algorithm. The results show that our approach performs better as compared to other techniques.
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Acknowledgments
This work was supported by the Industrial Strategic Technology Development Program (No. 10043907, Development of high performance IoT device and Open Platform with Intelligent Software), and this research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (NIPA-2014-H0301-14-1048) supervised by the NIPA (National IT Industry Promotion Agency).
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Ahmad, R., Kim, D. An Extended Self-Organizing Map based on 2-opt algorithm for solving symmetrical Traveling Salesperson Problem. Neural Comput & Applic 26, 987–994 (2015). https://doi.org/10.1007/s00521-014-1773-z
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DOI: https://doi.org/10.1007/s00521-014-1773-z