Blended selection in Ant Colony Optimization for solving Travelling Salesman Problem

2022 IEEE World Conference on Applied Intelligence and Computing (AIC) Pub Date : 2022-06-17 DOI:10.1109/AIC55036.2022.9848836

Nidhi Yadav, Probhat Pachung, Vani Agrawal, Jagdish Chand Bansal

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Abstract

TSP is one of the most well-known combinatorial optimization problems. Ant Colony optimization is highly recommended to solve discrete optimization problems whereas the selection strategy plays a crucial role in the performance of ACO while solving Travelling Salesman Problem (TSP). There are many selection strategies in ACO to solve TSP, such as roulette wheel selection, ranking selection and annealing selection etc. In ACO, the roulette wheel selection is primarily concerned with exploitation, whereas rank selection is influenced by exploration. Therefore, in this paper, a blend of both roulette wheel and ranking selection is proposed as a new selection strategy in ACO. The proposed selection method is tested over 12 standard TSP instances collected from TSP library TSPLIB. The best results obtained from the above mentioned selection method has been recorded and compared with other three selection methods. The experimental results show that the proposed selection method outperformed with other considered selection methods.

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蚁群优化中的混合选择求解旅行商问题

TSP是最著名的组合优化问题之一。蚁群算法是解决离散优化问题的常用方法，而蚁群算法在求解旅行商问题(TSP)时，选择策略对蚁群算法的性能起着至关重要的作用。蚁群算法求解TSP有许多选择策略，如轮盘选择、排序选择和退火选择等。在蚁群算法中，轮盘选择主要与开发有关，而等级选择则受探索的影响。因此，本文提出了轮盘和排序选择的混合选择策略作为蚁群算法中的一种新的选择策略。通过从TSP库TSPLIB中收集的12个标准TSP实例对所提出的选择方法进行了测试。记录了上述选择方法获得的最佳结果，并与其他三种选择方法进行了比较。实验结果表明，所提出的选择方法优于其他考虑的选择方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE World Conference on Applied Intelligence and Computing (AIC)

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