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A series of standard implementations have been created by leading researchers, "intended for use both as a baseline for performance testing of improvements to the technique, as well as to represent PSO to the wider optimization community. Having a well-known, strictly-defined standard algorithm provides a valuable point of comparison which can be used throughout the field of research to better test new advances."<ref name=bratton2007/> The latest is Standard PSO 2011 (SPSO-2011).<ref name=Zambrano-Bigiarini2013/>
In addition, some PSO variants have been developed to solve large-scale global optimization (LSGO) problems with more than 1000 dimensions. Representative variants include competitive swarm optimizer (CSO) and level-based learning swarm optimizer (LLSO)<ref name=llso/>. Recently, PSO has also been extended to solve multi-agent consensus-based distributed optimization problems, e.g., multi-agent consensus-based PSO with with adaptive internal and external learning (MASOIE) <ref name=masoie/>, etc.
=== Hybridization ===
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<ref name="nobile2015">{{cite conference | first1=M.S | last1=Nobile | title=Proactive particles in swarm optimization: a self-tuning algorithm based on fuzzy logic | last2=Pasi | first2=G. | last3=Cazzaniga | first3=P. | last4=Besozzi | first4=D. | last5=Colombo | first5=R. | last6=Mauri | first6=G. | book-title=Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Istanbul (Turkey) | year=2015 | pages=1–8| doi=10.1109/FUZZ-IEEE.2015.7337957 }}</ref>
<ref name=llso>{{cite journal
|last1=Yang
|first1=Q.
|last2=CHEN
|first2=W-N.
|last3=Deng
|first3=J-D.
|last4=Li
|first4=Y.
|last5=Gu
|first5=T.
|last6=Zhang
|first6=J.
|title=A Level-based Learning Swarm Optimizer for Large Scale Optimization
|year=2018
|volume=22
|issue=4
|pages=578-594
|doi=10.1109/TEVC.2017.2743016
|url=https://ieeexplore.ieee.org/document/8025724
|dead-url=no
}}</ref>
<ref name=masoie>{{cite journal
|last1=Chen
|first1=T-Y.
|last2=Chen
|first2=W-N.
|last3=Wei
|first3=F-F.
|last4=Hu
|first4=X-M.
|last5=Zhang
|first5=J.
|title=Multi-Agent Swarm Optimization With Adaptive Internal and External Learning for Complex Consensus-Based Distributed Optimization
|year=2024
|doi=10.1109/TEVC.2024.3380436
|url=https://ieeexplore.ieee.org/document/10477458
|dead-url=no
}}</ref>
}}
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