Kai Chen;Qingjun Qu;Feng Zhu;Zhengming Yi;Wenjie Tang
{"title":"CPLNS:面向大规模多代理路径查找的合作并行大型邻域搜索","authors":"Kai Chen;Qingjun Qu;Feng Zhu;Zhengming Yi;Wenjie Tang","doi":"10.1109/TPDS.2024.3408030","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 11","pages":"2069-2086"},"PeriodicalIF":5.6000,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"CPLNS: Cooperative Parallel Large Neighborhood Search for Large-Scale Multi-Agent Path Finding\",\"authors\":\"Kai Chen;Qingjun Qu;Feng Zhu;Zhengming Yi;Wenjie Tang\",\"doi\":\"10.1109/TPDS.2024.3408030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. 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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.
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.