Knowledge-assisted evolutionary task scheduling for hierarchical multiagent systems with transferable surrogates

IF 8.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tonghao Wang , Xingguang Peng , Xiaokang Lei , Handing Wang , Yaochu Jin
{"title":"Knowledge-assisted evolutionary task scheduling for hierarchical multiagent systems with transferable surrogates","authors":"Tonghao Wang ,&nbsp;Xingguang Peng ,&nbsp;Xiaokang Lei ,&nbsp;Handing Wang ,&nbsp;Yaochu Jin","doi":"10.1016/j.swevo.2025.102107","DOIUrl":null,"url":null,"abstract":"<div><div>Task scheduling is a primary step of a hierarchical multiagent system (HMAS) before solving tasks, presenting significant challenges due to its NP-hard complexity and variable-size decision space with different numbers of decision variables. This variability arises because a key decision is determining the number of agents to deploy, which directly affects the dimension of the decision vector. Evolutionary algorithms (EAs) have been widely adopted in addressing the task scheduling problem for HMAS due to their ability to solve NP-hard problems. However, applying conventional fixed-length EAs to such problems often necessitates techniques like expanding the decision space, which negatively impacts search efficiency. Meanwhile, the evaluations of the candidate solutions need physics-based simulations with complex dynamics, which require high computational costs. To solve the HMAS task scheduling problem efficiently, our approach leverages domain knowledge by a genetic programming framework alongside a knowledge-data dual-driven surrogate, which avoids searching in expanded decision spaces and facilitates low-cost evaluation. Notably, the proposed surrogate model can be easily transferred among different task settings, further decreasing the computational load in deploying the HMAS in real-world applications. The effectiveness of the proposed algorithm is validated through extensive simulations on an unmanned ground vehicle/unmanned aerial vehicle (UGV/UAV) cooperation system, showcasing superior efficiency and efficacy. Moreover, the proposed algorithm is also validated in a real-world multi-robot system, further demonstrating the efficacy and efficiency of the method, as well as the transferability of the proposed surrogate model.</div></div>","PeriodicalId":48682,"journal":{"name":"Swarm and Evolutionary Computation","volume":"98 ","pages":"Article 102107"},"PeriodicalIF":8.5000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swarm and Evolutionary Computation","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210650225002652","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Task scheduling is a primary step of a hierarchical multiagent system (HMAS) before solving tasks, presenting significant challenges due to its NP-hard complexity and variable-size decision space with different numbers of decision variables. This variability arises because a key decision is determining the number of agents to deploy, which directly affects the dimension of the decision vector. Evolutionary algorithms (EAs) have been widely adopted in addressing the task scheduling problem for HMAS due to their ability to solve NP-hard problems. However, applying conventional fixed-length EAs to such problems often necessitates techniques like expanding the decision space, which negatively impacts search efficiency. Meanwhile, the evaluations of the candidate solutions need physics-based simulations with complex dynamics, which require high computational costs. To solve the HMAS task scheduling problem efficiently, our approach leverages domain knowledge by a genetic programming framework alongside a knowledge-data dual-driven surrogate, which avoids searching in expanded decision spaces and facilitates low-cost evaluation. Notably, the proposed surrogate model can be easily transferred among different task settings, further decreasing the computational load in deploying the HMAS in real-world applications. The effectiveness of the proposed algorithm is validated through extensive simulations on an unmanned ground vehicle/unmanned aerial vehicle (UGV/UAV) cooperation system, showcasing superior efficiency and efficacy. Moreover, the proposed algorithm is also validated in a real-world multi-robot system, further demonstrating the efficacy and efficiency of the method, as well as the transferability of the proposed surrogate model.
具有可转移代理的分层多智能体系统的知识辅助进化任务调度
任务调度是分层多智能体系统(HMAS)在解决任务之前的首要步骤,由于其NP-hard复杂性和不同决策变量数量的变大小决策空间而面临重大挑战。出现这种可变性是因为一个关键决策是确定要部署的代理的数量,这直接影响决策向量的维度。进化算法因其解决np困难问题的能力而被广泛应用于HMAS的任务调度问题。然而,将传统的定长ea应用于此类问题通常需要扩展决策空间等技术,这会对搜索效率产生负面影响。同时,候选解的评估需要基于复杂动力学的物理模拟,这需要很高的计算成本。为了有效地解决HMAS任务调度问题,我们的方法通过遗传规划框架和知识-数据双驱动代理来利用领域知识,避免了在扩展的决策空间中搜索,并促进了低成本的评估。值得注意的是,所提出的代理模型可以很容易地在不同的任务设置之间转移,从而进一步减少了在实际应用程序中部署HMAS的计算负荷。通过对地面无人车/无人机(UGV/UAV)协同系统的大量仿真,验证了该算法的有效性,显示出卓越的效率和功效。此外,该算法还在一个真实的多机器人系统中进行了验证,进一步证明了该方法的有效性和效率,以及所提出的代理模型的可移植性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信