CPLNS:面向大规模多代理路径查找的合作并行大型邻域搜索

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Parallel and Distributed Systems Pub Date : 2024-06-11 DOI:10.1109/TPDS.2024.3408030
Kai Chen;Qingjun Qu;Feng Zhu;Zhengming Yi;Wenjie Tang
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引用次数: 0

摘要

大规模多代理寻路(MAPF)问题是组合优化领域的一项重大挑战。目前,接近最优的先进算法之一是大型邻域搜索(LNS),它可以处理数千个代理的实例。虽然基于多个独立 LNS 求解器的基本组合并行搜索提高了速度和鲁棒性,但随着 CPU 内核的增加,它也遇到了可扩展性问题。为解决这一限制,我们提出了合作并行 LNS(CPLNS)算法,旨在提高并行效率。合作并行搜索的主要挑战在于设计合适的组合和合作策略,以平衡搜索的多样化和集约化。为此,我们首先分析了 LNS 的特点。然后,我们引入了一种灵活的基于组的合作并行策略,即在每个组内共享当前最佳解决方案以帮助强化,同时通过独立的组计算保持多样化。此外,我们还通过整合基于模拟退火的 LNS 和有界次优单个代理寻路来增强搜索多样化。我们还引入了基于规则的组合构建方法,以简化参数设置并提高搜索效率。最后,我们通过共享数据过滤技术和优化数据结构提高了通信和内存效率。在 33 个地图、825 个实例的基准测试中,CPLNS 在 32 核机器上的中位速度提高了 21.95 倍,96.97% 的案例在 5 分钟内得到解决,平均次优化得分从 1.728 降至 1.456。此外,多达 10,000 个代理的测试验证了 CPLNS 对大规模 MAPF 问题的可扩展性。
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CPLNS: Cooperative Parallel Large Neighborhood Search for Large-Scale Multi-Agent Path Finding
The large-scale Multi-Agent Path Finding (MAPF) problem presents a significant challenge in combinatorial optimization. Currently, one of the advanced, near-optimal algorithms is Large Neighborhood Search (LNS), which can handle instances with thousands of agents. Although a basic portfolio parallel search based on multiple independent LNS solvers enhances speed and robustness, it encounters scalability issues with increasing CPU cores. To address this limitation, we propose the Cooperative Parallel LNS (CPLNS) algorithm, aimed at boosting parallel efficiency. The main challenge in cooperative parallel search lies in designing suitable portfolio and cooperative strategies that balance search diversification and intensification. To address this, we first analyze the characteristics of LNS. We then introduce a flexible group-based cooperative parallel strategy, where the current best solution is shared within each group to aid intensification, while maintaining diversification through independent group computations. Furthermore, we augment search diversification by integrating a simulated annealing-based LNS and bounded suboptimal single-agent pathfinding. We also introduce a rule-based methodology for portfolio construction to simplify parameter settings and improve search efficiency. Finally, we enhance communication and memory efficiency through a shared data filtering technique and optimized data structures. In benchmarks on 33 maps with 825 instances, CPLNS achieved a median speedup of 21.95 on a 32-core machine, solving 96.97% of cases within five minutes and reducing the average suboptimality score from 1.728 to 1.456. Additionally, tests with up to 10,000 agents verify CPLNS's scalability for large-scale MAPF problems.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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