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Updated: Jul 14, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Study on a hybrid algorithm combining enhanced ant colony optimization and double improved simulated annealing via
Tan Hao1, Wu Yingnian1, Zhang Jiaxing1
1School of Automation, Beijing Information Science and Technology University, Beijing, China.
Abstract:
In the process of solving the Traveling Salesman Problem (TSP), both Ant Colony Optimization and simulated annealing exhibit different limitations depending on the dataset. This article aims to address these limitations by improving and combining these two algorithms using the clustering method. The problems tackled include Ant Colony Optimization's susceptibility to stagnation, slow convergence, excessive computations, and local optima, as well as simulated annealing's slow convergence and limited local search capability. By conducting tests on various TSPLIB datasets, the algorithm proposed in this article demonstrates improved convergence speed and solution quality compared to traditional algorithms. Furthermore, it exhibits certain advantages over other existing improved algorithms. Finally, this article applies this algorithm to logistics transportation, yielding excellent results.
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