{"title":"基于多路径岛的k -最多元近最短路径遗传算法","authors":"Harish Sharma, Edgar Galván, Peter Mooney","doi":"10.1016/j.ins.2025.122495","DOIUrl":null,"url":null,"abstract":"<div><div>Modern routing applications, such as those used for vehicle navigation and emergency response routing, often require access to multiple optimal paths/routes rather than relying on a single optimal solution. However, existing methods typically struggle to balance optimality and diversity within the paths they generate. To address this challenge, we introduce the MultiPath Island-Based Genetic Algorithm (MIBGA) for solving the K-Most Diverse Near-Shortest Paths (KMDNSP) problem, with an emphasis on promoting both path diversity and computation of near-optimal paths. MIBGA is a Parallel Genetic Algorithm (PGA) based on the island model, and our approach incorporates novel migration and selection strategies that preserve diversity across subpopulations of path solutions. Experimental results on large, complex real-world road networks from Arizona, Washington, and Kansas demonstrate MIBGAs superior performance in terms of solution diversity, computational efficiency, and convergence speed compared to other well-established Genetic Algorithm (GA) based approaches. The results of our work further highlight the potential of GAs for addressing complex alternate routing problems in practical real-world settings.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"719 ","pages":"Article 122495"},"PeriodicalIF":6.8000,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MultiPath Island-Based Genetic Algorithm for the K-Most Diverse Near-Shortest Paths\",\"authors\":\"Harish Sharma, Edgar Galván, Peter Mooney\",\"doi\":\"10.1016/j.ins.2025.122495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Modern routing applications, such as those used for vehicle navigation and emergency response routing, often require access to multiple optimal paths/routes rather than relying on a single optimal solution. However, existing methods typically struggle to balance optimality and diversity within the paths they generate. To address this challenge, we introduce the MultiPath Island-Based Genetic Algorithm (MIBGA) for solving the K-Most Diverse Near-Shortest Paths (KMDNSP) problem, with an emphasis on promoting both path diversity and computation of near-optimal paths. MIBGA is a Parallel Genetic Algorithm (PGA) based on the island model, and our approach incorporates novel migration and selection strategies that preserve diversity across subpopulations of path solutions. Experimental results on large, complex real-world road networks from Arizona, Washington, and Kansas demonstrate MIBGAs superior performance in terms of solution diversity, computational efficiency, and convergence speed compared to other well-established Genetic Algorithm (GA) based approaches. The results of our work further highlight the potential of GAs for addressing complex alternate routing problems in practical real-world settings.</div></div>\",\"PeriodicalId\":51063,\"journal\":{\"name\":\"Information Sciences\",\"volume\":\"719 \",\"pages\":\"Article 122495\"},\"PeriodicalIF\":6.8000,\"publicationDate\":\"2025-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Information Sciences\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0020025525006279\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025525006279","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
MultiPath Island-Based Genetic Algorithm for the K-Most Diverse Near-Shortest Paths
Modern routing applications, such as those used for vehicle navigation and emergency response routing, often require access to multiple optimal paths/routes rather than relying on a single optimal solution. However, existing methods typically struggle to balance optimality and diversity within the paths they generate. To address this challenge, we introduce the MultiPath Island-Based Genetic Algorithm (MIBGA) for solving the K-Most Diverse Near-Shortest Paths (KMDNSP) problem, with an emphasis on promoting both path diversity and computation of near-optimal paths. MIBGA is a Parallel Genetic Algorithm (PGA) based on the island model, and our approach incorporates novel migration and selection strategies that preserve diversity across subpopulations of path solutions. Experimental results on large, complex real-world road networks from Arizona, Washington, and Kansas demonstrate MIBGAs superior performance in terms of solution diversity, computational efficiency, and convergence speed compared to other well-established Genetic Algorithm (GA) based approaches. The results of our work further highlight the potential of GAs for addressing complex alternate routing problems in practical real-world settings.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.