{"title":"改进旅行推销员问题的近邻启发式搜索算法","authors":"Md. Ziaur Rahman, Sakibur Rahamn Sheikh, Ariful Islam, Md. Azizur Rahman","doi":"10.38032/jea.2024.01.004","DOIUrl":null,"url":null,"abstract":"The Traveling Salesman Problem (TSP) is classified as a non-deterministic polynomial (NP) hard problem, which has found widespread application in several scientific and technological domains. Due to its NP-hard nature, it is very hard to solve effectively and efficiently. Despite this rationale, a multitude of optimization approaches have been proposed and developed by scientists and researchers during the last several decades. Among these several algorithms, heuristic approaches are deemed appropriate for addressing this intricate issue. One of the simplest and most easily implementable heuristic algorithms for TSP is the nearest neighbor algorithm (NNA). However, its solution quality suffers owing to randomness in the optimization process. To address this issue, this study proposes a deterministic NNA for solving symmetric TSP. It is an improved version of NNA, which starts with the shortest edge consisting of two cities and then repeatedly includes the closest city on the route until an effective route is established. The simulation is conducted on 20 benchmark symmetric TSP datasets obtained from TSPLIB. The simulation results provide evidence that the improved NNA outperforms the basic NNA throughout most of the datasets in terms of solution quality as well as computational time.","PeriodicalId":509159,"journal":{"name":"Journal of Engineering Advancements","volume":"54 43","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improvement of the Nearest Neighbor Heuristic Search Algorithm for Traveling Salesman Problem\",\"authors\":\"Md. Ziaur Rahman, Sakibur Rahamn Sheikh, Ariful Islam, Md. Azizur Rahman\",\"doi\":\"10.38032/jea.2024.01.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Traveling Salesman Problem (TSP) is classified as a non-deterministic polynomial (NP) hard problem, which has found widespread application in several scientific and technological domains. Due to its NP-hard nature, it is very hard to solve effectively and efficiently. Despite this rationale, a multitude of optimization approaches have been proposed and developed by scientists and researchers during the last several decades. Among these several algorithms, heuristic approaches are deemed appropriate for addressing this intricate issue. One of the simplest and most easily implementable heuristic algorithms for TSP is the nearest neighbor algorithm (NNA). However, its solution quality suffers owing to randomness in the optimization process. To address this issue, this study proposes a deterministic NNA for solving symmetric TSP. It is an improved version of NNA, which starts with the shortest edge consisting of two cities and then repeatedly includes the closest city on the route until an effective route is established. The simulation is conducted on 20 benchmark symmetric TSP datasets obtained from TSPLIB. The simulation results provide evidence that the improved NNA outperforms the basic NNA throughout most of the datasets in terms of solution quality as well as computational time.\",\"PeriodicalId\":509159,\"journal\":{\"name\":\"Journal of Engineering Advancements\",\"volume\":\"54 43\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Engineering Advancements\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.38032/jea.2024.01.004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Engineering Advancements","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.38032/jea.2024.01.004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
旅行推销员问题(TSP)被归类为非确定性多项式(NP)难题,已在多个科学和技术领域得到广泛应用。由于它的 NP 难性质,很难有效和高效地解决它。尽管如此,在过去的几十年里,科学家和研究人员还是提出并开发了许多优化方法。在这些算法中,启发式方法被认为是解决这一复杂问题的适当方法。用于 TSP 的最简单、最易实现的启发式算法之一是近邻算法 (NNA)。然而,由于优化过程中的随机性,其解决方案的质量受到影响。针对这一问题,本研究提出了一种用于求解对称 TSP 的确定性 NNA。它是 NNA 的改进版,从由两个城市组成的最短边开始,然后重复包括路线上最近的城市,直到建立有效路线。仿真在从 TSPLIB 获取的 20 个基准对称 TSP 数据集上进行。仿真结果表明,在大多数数据集中,改进后的 NNA 在求解质量和计算时间方面都优于基本 NNA。
Improvement of the Nearest Neighbor Heuristic Search Algorithm for Traveling Salesman Problem
The Traveling Salesman Problem (TSP) is classified as a non-deterministic polynomial (NP) hard problem, which has found widespread application in several scientific and technological domains. Due to its NP-hard nature, it is very hard to solve effectively and efficiently. Despite this rationale, a multitude of optimization approaches have been proposed and developed by scientists and researchers during the last several decades. Among these several algorithms, heuristic approaches are deemed appropriate for addressing this intricate issue. One of the simplest and most easily implementable heuristic algorithms for TSP is the nearest neighbor algorithm (NNA). However, its solution quality suffers owing to randomness in the optimization process. To address this issue, this study proposes a deterministic NNA for solving symmetric TSP. It is an improved version of NNA, which starts with the shortest edge consisting of two cities and then repeatedly includes the closest city on the route until an effective route is established. The simulation is conducted on 20 benchmark symmetric TSP datasets obtained from TSPLIB. The simulation results provide evidence that the improved NNA outperforms the basic NNA throughout most of the datasets in terms of solution quality as well as computational time.