Application of proposed hybrid active genetic algorithm for optimization of traveling salesman problem

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.

Automated summary

In the era of technological development, finding precise solutions to complex problems becomes more important. This study proposes a new method to improve the convergence rate by incorporating a unique feature called 'sub-tour division', which shows higher accuracy and robustness in solving the traveling salesman problem.