{"title":"群体密度对集体跟踪性能的影响","authors":"Hian Lee Kwa, Julien Philippot, Roland Bouffanais","doi":"10.1007/s11721-023-00225-4","DOIUrl":null,"url":null,"abstract":"How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different ‘phases’ corresponding to markedly distinct group dynamics. We identify a ‘transition’ phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system’s performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm’s behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.","PeriodicalId":51284,"journal":{"name":"Swarm Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":2.1000,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Effect of swarm density on collective tracking performance\",\"authors\":\"Hian Lee Kwa, Julien Philippot, Roland Bouffanais\",\"doi\":\"10.1007/s11721-023-00225-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different ‘phases’ corresponding to markedly distinct group dynamics. We identify a ‘transition’ phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system’s performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. 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Effect of swarm density on collective tracking performance
How does the size of a swarm affect its collective action? Despite being arguably a key parameter, no systematic and satisfactory guiding principles exist to select the number of units required for a given task and environment. Even when limited by practical considerations, system designers should endeavor to identify what a reasonable swarm size should be. Here, we show that this fundamental question is closely linked to that of selecting an appropriate swarm density. Our analysis of the influence of density on the collective performance of a target tracking task reveals different ‘phases’ corresponding to markedly distinct group dynamics. We identify a ‘transition’ phase, in which a complex emergent collective response arises. Interestingly, the collective dynamics within this transition phase exhibit a clear trade-off between exploratory actions and exploitative ones. We show that at any density, the exploration–exploitation balance can be adjusted to maximize the system’s performance through various means, such as by changing the level of connectivity between agents. While the density is the primary factor to be considered, it should not be the sole one to be accounted for when sizing the system. Due to the inherent finite-size effects present in physical systems, we establish that the number of constituents primarily affects system-level properties such as exploitation in the transition phase. These results illustrate that instead of learning and optimizing a swarm’s behavior for a specific set of task parameters, further work should instead concentrate on learning to be adaptive, thereby endowing the swarm with the highly desirable feature of being able to operate effectively over a wide range of circumstances.
期刊介绍:
Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research
and developments in the multidisciplinary field of swarm intelligence. The journal publishes
original research articles and occasional review articles on theoretical, experimental and/or
practical aspects of swarm intelligence. All articles are published both in print and in electronic
form. There are no page charges for publication. Swarm Intelligence is published quarterly.
The field of swarm intelligence deals with systems composed of many individuals that coordinate
using decentralized control and self-organization. In particular, it focuses on the collective
behaviors that result from the local interactions of the individuals with each other and with their
environment. It is a fast-growing field that encompasses the efforts of researchers in multiple
disciplines, ranging from ethology and social science to operations research and computer
engineering.
Swarm Intelligence will report on advances in the understanding and utilization of swarm
intelligence systems, that is, systems that are based on the principles of swarm intelligence. The
following subjects are of particular interest to the journal:
• modeling and analysis of collective biological systems such as social insect colonies, flocking
vertebrates, and human crowds as well as any other swarm intelligence systems;
• application of biological swarm intelligence models to real-world problems such as distributed
computing, data clustering, graph partitioning, optimization and decision making;
• theoretical and empirical research in ant colony optimization, particle swarm optimization,
swarm robotics, and other swarm intelligence algorithms.