Optimal algorithm of the traveling salesman problem for tourist location selection

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Dynamic programming achieved optimal results in distance, time, and cost for tourist locations, making it practical for real-world applications.
Stochastic algorithms like simulated annealing performed competitively alongside genetic algorithm and particle swarm optimization for smaller datasets.
Deterministic methods included greedy algorithm, brute force, and branch and bound, providing a robust comparison for tourism optimization.
This study highlights the effective application of various algorithms in minimizing travel distance and cost across multiple tourist locations.

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Analysis reveals dynamic programming offers optimal solutions for tourism route planning, indicating efficient algorithm usage.

Hossen et al. (2025) studied this question.

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