Abstract
In the technological development era, the solution to various complex problems is uncertain, and more attention should be paid for changing certain conditions. Therefore, a precise solution to a complex problem is critical. The traveling salesman problem (TSP) is often fuse for an outbreak of a better solution. Inspired by the successful genetic algorithm (GA) applications, this study proposes a new approach to improve the convergence rate by incorporating a unique feature, namely 'sub-tour division'. The new method consists of multiple zones of TSPs (i.e., active and inactive), which are used to sort and group the critical region for finding the solutions. To illustrate the performance of the new approach, the traveling distance between various cities in India is considered as a problem. The simulation findings show that the new approach provides a more accurate and robust solution to a complex problem than alternative methods.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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The corresponding author is grateful to the co-authors for their suggestions at every study stage.
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R.J. and K.P.S. conceptualized and designed the study, analyzed the data, and drafted and revised the manuscript. R.J., A.M., K.B.R., M.L.M.E., G.S.D. and X.G. reviewed and critically revised the manuscript. All authors approved the submission of the final manuscript.
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Jain, R., Singh, K.P., Meena, A. et al. Application of proposed hybrid active genetic algorithm for optimization of traveling salesman problem. Soft Comput 27, 4975–4985 (2023). https://doi.org/10.1007/s00500-022-07581-z
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DOI: https://doi.org/10.1007/s00500-022-07581-z