Abstract
Travelling salesman problem (TSP) is an NP-hard combinatorial problem and exhaustive search for an optimal solution is computationally intractable. The present work proposes a discrete version of Whale optimization algorithm (WOA) to find an optimal tour for a given travelling salesman network. Further, a greedy technique is incorporated in WOA (GWOA) to generate new tours which avoid the creation and analysis of non-optimal tours during successive iterations. Standard TSPLIB dataset is used for validation of the proposed technique. Further robustness of GWOA is evaluated on random TSP walks. It is observed from the results that proposed GWOA provides near optimal solution in less number of iterations as compared to WOA and Genetic algorithm (GA) for a given network of TSP.
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Gupta, R., Shrivastava, N., Jain, M., Singh, V., Rani, A. (2018). Greedy WOA for Travelling Salesman Problem. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_32
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DOI: https://doi.org/10.1007/978-981-13-1810-8_32
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