Editing Swarm behaviour (section)

====Particle swarm optimization====
{{Main|Particle swarm optimization}}
[[Particle swarm optimization]] is another algorithm widely used to solve problems related to swarms. It was developed in 1995 by [[James Kennedy (social psychologist)|Kennedy]] and [[Russell C. Eberhart|Eberhart]] and was first aimed at [[computer simulation|simulating]] the social behaviour and choreography of bird flocks and fish schools.<ref name=kennedy95particle>
{{cite conference
|last1=Kennedy
|first1=J.
|last2=Eberhart
|first2=R.
|title=Particle Swarm Optimization
|book-title=Proceedings of IEEE International Conference on Neural Networks
|year=1995
|volume=IV
|pages=1942–1948
}}</ref><ref name=kennedy97particle>
{{cite conference
|last1=Kennedy
|first1=J.
|title=The particle swarm: social adaptation of knowledge
|book-title=Proceedings of IEEE International Conference on Evolutionary Computation
|year=1997
|pages=303–308
}}</ref> The algorithm was simplified and it was observed to be performing optimization. The system initially seeds a population with random solutions. It then searches in the [[candidate solution|problem space]] through successive generations using [[stochastic optimization]] to find the best solutions. The solutions it finds are called [[Point particle|particles]]. Each particle stores its position as well as the best solution it has achieved so far. The particle swarm optimizer tracks the [[maxima and minima|best local value]] obtained so far by any particle in the local neighbourhood. The remaining particles then move through the problem space following the lead of the optimum particles. At each time iteration, the particle swarm optimiser accelerates each particle toward its optimum locations according to simple [[mathematical formulae|mathematical rules]]. In a related approach, Shvalb et al. (2024) introduced a statistical-physics-based framework for controlling large-scale multi-robot systems. By modeling robots as particles within a statistical ensemble, the study leverages macroscopic parameters—such as density and flow fields—to guide collective behavior without the need for individual identification or direct communication between agents. This method enables scalable and robust control of robot swarms, drawing conceptual parallels to particle swarm optimization by utilizing global information to influence local agent dynamics.<ref name=shvalb2024>
{{cite journal
 |last1=Shvalb
 |first1=Nir
 |last2=Hacohen
 |first2=Shlomi
 |last3=Medina
 |first3=Oded
 |title=Statistical Robotics: Controlling Multi Robot Systems using Statistical-physics
 |journal=IEEE Access
 |year=2024
 |volume=12
 |pages=134739–134753
 |doi=10.1109/ACCESS.2024.3406599
 |bibcode=2024IEEEA..12m4739S
 |url=https://www.researchgate.net/publication/380947211
 |access-date=2025-05-13
}}
</ref> 
Particle swarm optimization has been applied in many areas. It has few parameters to adjust, and a version that works well for a specific applications can also work well with minor modifications across a range of related applications.<ref>Hu X [http://www.swarmintelligence.org/tutorials.php Particle swarm optimization: Tutorial]. Retrieved 15 December 2010.</ref> A book by Kennedy and Eberhart describes some philosophical aspects of particle swarm optimization applications and swarm intelligence.<ref name=kennedy01swarm>
{{cite book
|title=Swarm Intelligence
|last1=Kennedy
|first1=J.
|last2=Eberhart
|first2=R.C.
|year=2001
|publisher=Morgan Kaufmann
|isbn=978-1-55860-595-4
}}</ref> An extensive survey of applications is made by Poli.<ref name=poli07analysis>{{cite journal
|last=Poli
|first=R.
|url=http://cswww.essex.ac.uk/technical-reports/2007/tr-csm469.pdf
|title=An analysis of publications on particle swarm optimisation applications
|journal=Technical Report CSM-469
|year=2007
|access-date=15 December 2010
|archive-date=16 July 2011
|archive-url=https://web.archive.org/web/20110716231935/http://cswww.essex.ac.uk/technical-reports/2007/tr-csm469.pdf
|url-status=dead
}}</ref><ref name=poli08analysis>
{{cite journal
|last=Poli
|first=R.
|url=http://downloads.hindawi.com/journals/jaea/2008/685175.pdf
|title=Analysis of the publications on the applications of particle swarm optimisation
|journal=Journal of Artificial Evolution and Applications
|year=2008
|volume=2008
|pages=1–10
|doi=10.1155/2008/685175
|doi-access=free
}}</ref>