A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

Jiang, Yirui; Zhao, S. J.; Li, Hongwei; Qin, Yulu; Yang, Xiaoyue

doi:10.1007/s40747-022-00880-w

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…For instance, the AZP algorithm (Openshaw, 1977) sought to iteratively refine solutions while maintaining spatial connectivity, yet tended to get stuck in local optima. To address this, methods like tabu search (Baldassarre et al, 2023;Duque & Church, 2004), simulated annealing (Jiang et al, 2023;Ko et al, 2015), and ant colony optimization (Mimis et al, 2012) were introduced to enhance solution diversity and partitioning. Additionally, tree-based heuristic (Assunção et al, 2006;Côme, 2024;Guo, 2008) approaches have been developed to simplify neighborhood searches, structuring spatial units into connected graphs for improved partitioning, all while preserving connectivity.…”

Section: Introductionmentioning

confidence: 99%

A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.

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Section: Introductionmentioning

confidence: 99%

A survey on applications of reinforcement learning in spatial resource allocation

Zhang,

Wang,

Mango

et al. 2024

Comput.Urban Sci.

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An Optimal Individual Selection Algorithm Based on "Layer Proximity and Branch Distance Function"

bingqin

yingjian²

2023

Preprint

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Using heuristic method to automatically generate test cases is a research hotspot at present. Although its advantages are obvious, it is slightly insufficient in the selection of optimal individuals. In this paper, aiming at the problems existing in the evaluation and selection of the optimal individual at present, based on the comprehensive analysis of the characteristics of layer proximity and branch distance function, a test case evaluation algorithm combining layer proximity and branch distance function is proposed. The basic idea of this algorithm is to select the individuals with high proximity between the actual execution path and the target path, and then select the individuals with the smallest branch distance in these individuals, so as to obtain the individuals with the best navigation ability. Experiments show that the proposed algorithm can quickly find test cases, especially for the test case generation of multi-layer nested programs.

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A hybrid spectral clustering simulated annealing algorithm for the street patrol districting problem

Cited by 2 publications

References 49 publications

A survey on applications of reinforcement learning in spatial resource allocation

A survey on applications of reinforcement learning in spatial resource allocation

An Optimal Individual Selection Algorithm Based on "Layer Proximity and Branch Distance Function"

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