MultiPath Island-Based Genetic Algorithm for the K-Most Diverse Near-Shortest Paths

IF 6.8 1区 计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS
Harish Sharma, Edgar Galván, Peter Mooney
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引用次数: 0

Abstract

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.
基于多路径岛的k -最多元近最短路径遗传算法
现代路由应用程序,例如用于车辆导航和应急响应路由的应用程序,通常需要访问多个最佳路径/路线,而不是依赖于单一的最佳解决方案。然而,现有的方法通常很难在它们生成的路径中平衡最优性和多样性。为了解决这一挑战,我们引入了基于多路径岛屿的遗传算法(MIBGA)来解决k -最多样化的近最短路径(KMDNSP)问题,重点是促进路径多样性和近最优路径的计算。MIBGA是一种基于岛屿模型的并行遗传算法(PGA),我们的方法结合了新的迁移和选择策略,以保持路径解决方案亚群的多样性。来自亚利桑那州、华盛顿州和堪萨斯州的大型复杂现实道路网络的实验结果表明,与其他成熟的基于遗传算法(GA)的方法相比,MIBGAs在解决方案多样性、计算效率和收敛速度方面具有优越的性能。我们的工作结果进一步强调了GAs在实际世界环境中解决复杂替代路由问题的潜力。
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来源期刊
Information Sciences
Information Sciences 工程技术-计算机:信息系统
CiteScore
14.00
自引率
17.30%
发文量
1322
审稿时长
10.4 months
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.
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