LEADER 03053nam 22005175 450 001 9911049111803321 005 20260102122917.0 010 $a981-9533-81-3 024 7 $a10.1007/978-981-95-3381-7 035 $a(CKB)44770133500041 035 $a(MiAaPQ)EBC32484311 035 $a(Au-PeEL)EBL32484311 035 $a(DE-He213)978-981-95-3381-7 035 $a(EXLCZ)9944770133500041 100 $a20260102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aParticle Swarm Optimizer and Multi-Objective Optimization /$fby Feng Pan, Qi Gao, Xiao-xue Feng, Wei-xing Li 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (319 pages) 225 1 $aMathematics and Statistics Series 311 08$a981-9533-80-5 327 $aIntroduction -- Overview of PSO -- Algorithm characteristics of PSO -- Sampling Distribution and Particle Trajectories in Standard PSO -- Stability analysis of the standard PSO. 330 $aThis book provides a comprehensive overview of the foundational attributes of the Particle Swarm Optimization(PSO) algorithm, including general descriptions, topological structures, evaluation metrics, and diversity. It explores in depth the issues of premature convergence and the kinematic characteristics of the Gbest (Global best), Pbest (Personal best), and standard particle models. The book also introduces a stability criterion based on dynamic time-varying systems and examines the Markov properties and convergence behavior of the standard PSO algorithm. For single-objective optimization problems, the book presents four paradigmatic design philosophies and enhancement strategies for PSO algorithms. In addressing multi-objective optimization challenges, it offers a systematic analysis and design methodology for multi-objective PSO. This book is ideal for researchers in the fields of swarm intelligence and optimization techniques. It aids scholars and professionals in gaining a deep understanding of swarm intelligence methodologies, with a particular focus on the systematic characteristics, stability, convergence, and other critical aspects of the PSO algorithm. This knowledge equips readers to navigate and contribute to the evolving field of swarm intelligence. 410 0$aMathematics and Statistics Series 606 $aComputational intelligence 606 $aMathematical optimization 606 $aComputational Intelligence 606 $aOptimization 615 0$aComputational intelligence. 615 0$aMathematical optimization. 615 14$aComputational Intelligence. 615 24$aOptimization. 676 $a006.3 700 $aPan$b Feng$01399766 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049111803321 996 $aParticle Swarm Optimizer and Multi-Objective Optimization$94531709 997 $aUNINA