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Particle swarm optimizer and multi-o...

Pan, Feng.

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Particle swarm optimizer and multi-objective optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Particle swarm optimizer and multi-objective optimization/ by Feng Pan ... [et al.].
other author:	Pan, Feng.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xii, 228 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Overview of PSO -- Algorithm characteristics of PSO -- Sampling Distribution and Particle Trajectories in Standard PSO -- Stability analysis of the standard PSO.
Contained By:	Springer Nature eBook
Subject:	Swarm intelligence. -
Online resource:	https://doi.org/10.1007/978-981-95-3381-7
ISBN:	9789819533817

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

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

577800
Swarm intelligence.

LC Class. No.: Q337.3

Dewey Class. No.: 006.3824

Particle swarm optimizer and multi-objective optimization
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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.
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650 0 $a Latent structure analysis. $3 539434
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Optimization. $3 891104
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