國立高雄大學圖資館 |

Language: English

Back

Particle swarm optimizer and multi-objective optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Particle swarm optimizer and multi-objective optimizationby Feng Pan ... [et al.].
other author:	Pan, Feng.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xii, 228 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Swarm intelligence.
Online resource:	https://doi.org/10.1007/978-981-95-3381-7
ISBN:	9789819533817$q(electronic bk.)

Particle swarm optimizer and multi-objective optimization
Particle swarm optimizer and multi-objective optimization[electronic resource] /by Feng Pan ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xii, 228 p. :ill., digital ;24 cm.

Introduction -- Overview of PSO -- Algorithm characteristics of PSO -- Sampling Distribution and Particle Trajectories in Standard PSO -- Stability analysis of the standard PSO.

This book provides a comprehensive overview of the foundational attributes of the Particle Swarm Optimization(PSO) algorithm, including general descriptions, topological structures, evaluation metrics, and diversity. It explores in depth the issues of premature convergence and the kinematic characteristics of the Gbest (Global best), Pbest (Personal best), and standard particle models. The book also introduces a stability criterion based on dynamic time-varying systems and examines the Markov properties and convergence behavior of the standard PSO algorithm. For single-objective optimization problems, the book presents four paradigmatic design philosophies and enhancement strategies for PSO algorithms. In addressing multi-objective optimization challenges, it offers a systematic analysis and design methodology for multi-objective PSO. This book is ideal for researchers in the fields of swarm intelligence and optimization techniques. It aids scholars and professionals in gaining a deep understanding of swarm intelligence methodologies, with a particular focus on the systematic characteristics, stability, convergence, and other critical aspects of the PSO algorithm. This knowledge equips readers to navigate and contribute to the evolving field of swarm intelligence.

ISBN: 9789819533817$q(electronic bk.)

Standard No.: 10.1007/978-981-95-3381-7doiSubjects--Topical Terms:

237730
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

Particle swarm optimizer and multi-objective optimization
LDR:02449nmm a2200325 a 4500 001 691936
003 DE-He213
005 20260102122917.0
006 m d
007 cr nn 008maaau
008 260527s2025 si s 0 eng d
020 $a 9789819533817$q(electronic bk.)
020 $a 9789819533800$q(paper)
024 7 $a 10.1007/978-981-95-3381-7 $2 doi
035 $a 978-981-95-3381-7
040 $a GP $c GP
041 0 $a eng
050 4 $a Q337.3
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3824 $2 23
090 $a Q337.3 $b .P273 2025
245 0 0 $a Particle swarm optimizer and multi-objective optimization $h [electronic resource] / $c by Feng Pan ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xii, 228 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Overview of PSO -- Algorithm characteristics of PSO -- Sampling Distribution and Particle Trajectories in Standard PSO -- Stability analysis of the standard PSO.
520 $a This book provides a comprehensive overview of the foundational attributes of the Particle Swarm Optimization(PSO) algorithm, including general descriptions, topological structures, evaluation metrics, and diversity. It explores in depth the issues of premature convergence and the kinematic characteristics of the Gbest (Global best), Pbest (Personal best), and standard particle models. The book also introduces a stability criterion based on dynamic time-varying systems and examines the Markov properties and convergence behavior of the standard PSO algorithm. For single-objective optimization problems, the book presents four paradigmatic design philosophies and enhancement strategies for PSO algorithms. In addressing multi-objective optimization challenges, it offers a systematic analysis and design methodology for multi-objective PSO. This book is ideal for researchers in the fields of swarm intelligence and optimization techniques. It aids scholars and professionals in gaining a deep understanding of swarm intelligence methodologies, with a particular focus on the systematic characteristics, stability, convergence, and other critical aspects of the PSO algorithm. This knowledge equips readers to navigate and contribute to the evolving field of swarm intelligence.
650 0 $a Swarm intelligence. $3 237730
650 0 $a Latent structure analysis. $3 240197
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Optimization. $3 274084
700 1 $a Pan, Feng. $3 246953
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-95-3381-7
950 $a Mathematics and Statistics (SpringerNature-11649)