國立高雄大學圖資館 |

登入

回首頁

Swarm Dynamics: Flocking and Communication-Based Optimization /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Swarm Dynamics: Flocking and Communication-Based Optimization /Jingcheng Lu.
作者:	Lu, Jingcheng,
面頁冊數:	1 electronic resource (158 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
附註:	Advisors: Tadmor, Eitan Committee members: Baeder, James D.; Konstantina, Trivisa; Cameron, Maria K.; Zenginoglu, Anil Colpan.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Applied mathematics.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30317164
ISBN:	9798379761738

Swarm Dynamics: Flocking and Communication-Based Optimization /
Lu, Jingcheng,

Swarm Dynamics: Flocking and Communication-Based Optimization /Jingcheng Lu. - 1 electronic resource (158 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

In this thesis, we discuss the swarm dynamics in the modeling of flocking behavior and in the application to numerical optimization. In general, the research of swarm dynamics deals with the collective behavior of multi-agent systems, where interactions between individuals would lead to the emergence of higher-level, self-organized structures of large crowd. Examples occur in a variety of research fields, including physics, biology, human society, and artificial intelligence, etc. Due to the wide range of applications, swarm dynamics has been an important research topic over the last decades. The first part of our study is devoted to the flocking dynamics driven by the Cucker-Smale system. The flocking behavior is addressed at particle and hydrodynamic levels for single- and multi-species. When studying the hydrodynamic model, a canonical issue is the lack of closure for pressure. The main contribution of our work is to prove the hydrodynamic alignment for multi-species without assuming specific thermodynamic closure. The proof is based on the graph connectivity and a weak dispersion bound. Besides the analytical results, we also discuss the simulation techniques for collective dynamics, including particle and hydrodynamic simulations. Numerical examples are presented to demonstrate the flocking behavior. The second part of the thesis focuses on the swarm-based approach for numerical optimization. Based on the idea of consensus formation, we propose a new Swarm-Based Gradient Descent (SBGD) method. To improve the flexibility of exploration, the method employs multiple interacting agents to search for solution. The key innovation of SBGD is to introduce individual masses, which distinguish the quality of different agents and dictate their choice of step size. Agents exchange information through dynamical mass transitions. As iterations proceed, the good agents will gradually become heavy `leaders' and help the crowd find better minimizers. The remarkable improvement of SBGD over the classical gradient descent method and the recently popular Adam method is verified in a series of benchmark tests.

English

ISBN: 9798379761738Subjects--Topical Terms:

377601
Applied mathematics.
Subjects--Index Terms:

Swarm dynamics

Swarm Dynamics: Flocking and Communication-Based Optimization /
LDR:03600nmm a22004213i 4500 001 676264
005 20250420214330.5
006 m o d
007 cr|nu||||||||
008 250901s2023 miu||||||m |||||||eng d
020 $a 9798379761738
035 $a (MiAaPQD)AAI30317164
035 $a AAI30317164
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Lu, Jingcheng, $e author. $3 989998
245 1 0 $a Swarm Dynamics: Flocking and Communication-Based Optimization / $c Jingcheng Lu.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 1 electronic resource (158 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisors: Tadmor, Eitan Committee members: Baeder, James D.; Konstantina, Trivisa; Cameron, Maria K.; Zenginoglu, Anil Colpan.
502 $b Ph.D. $c University of Maryland, College Park $d 2023.
520 $a In this thesis, we discuss the swarm dynamics in the modeling of flocking behavior and in the application to numerical optimization. In general, the research of swarm dynamics deals with the collective behavior of multi-agent systems, where interactions between individuals would lead to the emergence of higher-level, self-organized structures of large crowd. Examples occur in a variety of research fields, including physics, biology, human society, and artificial intelligence, etc. Due to the wide range of applications, swarm dynamics has been an important research topic over the last decades. The first part of our study is devoted to the flocking dynamics driven by the Cucker-Smale system. The flocking behavior is addressed at particle and hydrodynamic levels for single- and multi-species. When studying the hydrodynamic model, a canonical issue is the lack of closure for pressure. The main contribution of our work is to prove the hydrodynamic alignment for multi-species without assuming specific thermodynamic closure. The proof is based on the graph connectivity and a weak dispersion bound. Besides the analytical results, we also discuss the simulation techniques for collective dynamics, including particle and hydrodynamic simulations. Numerical examples are presented to demonstrate the flocking behavior. The second part of the thesis focuses on the swarm-based approach for numerical optimization. Based on the idea of consensus formation, we propose a new Swarm-Based Gradient Descent (SBGD) method. To improve the flexibility of exploration, the method employs multiple interacting agents to search for solution. The key innovation of SBGD is to introduce individual masses, which distinguish the quality of different agents and dictate their choice of step size. Agents exchange information through dynamical mass transitions. As iterations proceed, the good agents will gradually become heavy `leaders' and help the crowd find better minimizers. The remarkable improvement of SBGD over the classical gradient descent method and the recently popular Adam method is verified in a series of benchmark tests.
546 $a English
590 $a School code: 0117
650 4 $a Applied mathematics. $3 377601
650 4 $a Computer engineering. $3 212944
653 $a Swarm dynamics
653 $a Cucker-Smale system
653 $a Adam method
653 $a Benchmark tests
690 $a 0364
690 $a 0464
690 $a 0800
710 2 $a University of Maryland, College Park. $b Mathematics. $e degree granting institution. $3 989999
720 1 $a Tadmor, Eitan $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-01B.
790 $a 0117
791 $a Ph.D.
792 $a 2023
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30317164