Optimization of Saleman Travelling Problem Using Genetic Algorithm with Combination of Order and Random Crossover

2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) Pub Date : 2023-08-25 DOI:10.1109/ICCSCE58721.2023.10237137

Muhammad Firdaus Shafie, F. Ahmad, Muhammad Khusairi Osman, Ahmad Puad Ismail, K. A. Ahmad, S. Z. Yahaya, M. Idris, Anwar Hassan Ibrahim

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Abstract

Traveling salesman problem (TSP) is a problem of determining the shortest path for a salesman to take to visit all cities. Although a small number of cities is easy to solve, as the number of cities increases, it’s not possible to solve in polynomial time as it was a combinatorial nondeterministic polynomial (NP-hard) problem. Hence, this project is implementing a genetic algorithm (GA) to solve TSP using Python programming. The focus of this paper is to analyze the GA using order crossover (OX) and random crossover (RX) and propose a combination mechanism, direct combination (OX-RX) and Dynamic Linear combination (OXRX-Linear) to optimize TSP. We test GA for OX and RX in a random set of cities, up to 75 total cities. Then compare the result of the proposed combination OX-RX and OX-RXLinear. The result shows that both proposed combined mechanisms OX-RX and OX-RX-Linear improve the performance of GA in solving TSP.

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基于有序与随机交叉相结合的遗传算法优化销售员出行问题

旅行推销员问题(TSP)是确定一个推销员访问所有城市所采取的最短路径的问题。虽然少数城市很容易解决，但随着城市数量的增加，它不可能在多项式时间内解决，因为它是一个组合不确定性多项式(NP-hard)问题。因此，本项目使用Python编程实现遗传算法(GA)来解决TSP问题。本文重点分析了基于有序交叉(OX)和随机交叉(RX)的遗传算法，提出了直接组合(OX-RX)和动态线性组合(OXRX-Linear)优化TSP的组合机制。我们在一组随机的城市中测试了OX和RX的GA，总共有75个城市。然后比较所提出的OX-RX和OX-RXLinear组合的结果。结果表明，提出的OX-RX和OX-RX- linear组合机制提高了遗传算法求解TSP的性能。

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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2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE)

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