On the public citation graph

An Extended Particle Swarm Optimizer

https://doi.org/10.1109/ipdps.2005.101

0 signed · 23 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/bfb0040811openalex-snapshot

Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences

inferredcites →https://doi.org/10.1109/cec.1999.785513openalex-snapshot

The swarm and the queen: towards a deterministic and adaptive particle swarm optimization

inferredcites →https://doi.org/10.1109/cec.2000.870279openalex-snapshot

Comparing inertia weights and constriction factors in particle swarm optimization

inferredcites →https://doi.org/10.1109/cec.2002.1004493openalex-snapshot

Population structure and particle swarm performance

inferredcites →https://doi.org/10.1109/icec.1997.592326openalex-snapshot

The particle swarm: social adaptation of knowledge

inferredcites →https://doi.org/10.1109/icec.1998.699146openalex-snapshot

A modified particle swarm optimizer

inferredcites →https://doi.org/10.1109/icnn.1995.488968openalex-snapshot

Particle swarm optimization

inferredcites →https://doi.org/10.1109/ijcnn.2001.939004openalex-snapshot

Training product unit networks using cooperative particle swarm optimisers

inferredcites →https://doi.org/10.1109/mhs.1995.494215openalex-snapshot

A new optimizer using particle swarm theory

inferredcites →https://doi.org/10.1109/sis.2003.1202252openalex-snapshot

Watch thy neighbor or how the swarm can learn from its environment

inferredcites →https://doi.org/10.1109/smcia.2003.1231342openalex-snapshot

Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s12293-016-0182-5openalex-snapshot

A kernel extreme learning machine algorithm based on improved particle swam optimization

inferred← citeshttps://doi.org/10.1007/s12597-009-0004-3openalex-snapshot

The flexi-PSO: Towards a more flexible particle swarm optimizer

inferred← citeshttps://doi.org/10.1016/j.asoc.2007.01.009openalex-snapshot

The landscape adaptive particle swarm optimizer

inferred← citeshttps://doi.org/10.1016/s1005-8885(08)60095-3openalex-snapshot

Research of stochastic weight strategy for extended particle swarm optimizer

inferred← citeshttps://doi.org/10.1109/cec.2006.1688395openalex-snapshot

Locating All the Global Minima Using Multi-Species Particle Swarm Optimizer: The Inertia Weight and The Constriction Factor Variants

inferred← citeshttps://doi.org/10.1109/cec.2006.1688396openalex-snapshot

An Empirical Study on the Settings of Control Coefficients in Particle Swarm Optimization

inferred← citeshttps://doi.org/10.1109/icics.2005.1689001openalex-snapshot

A real application of extended particle swarm optimizer

inferred← citeshttps://doi.org/10.1109/miltechs.2015.7153727openalex-snapshot

Utilization of optimization methods cooperation for the improvement of optimization process

inferred← citeshttps://doi.org/10.1109/sii.2012.6427303openalex-snapshot

Diesel engine optimization control methods for reduction of exhaust emission and fuel consumption

inferred← citeshttps://doi.org/10.1145/2906150openalex-snapshot

Understanding the Limitations of Particle Swarm Algorithm for Dynamic Optimization Tasks

inferred← citeshttps://doi.org/10.14203/j.mev.2015.v6.31-38openalex-snapshot

Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle

inferred← citeshttps://doi.org/10.7763/ijcte.2009.v1.80openalex-snapshot

Particle Swarm Optimization Methods, Taxonomy and Applications

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.