Encyclopedia of Systems Biology

2013 Edition
| Editors: Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho, Hiroki Yokota

Particle Swarm Optimization (PSO)

  • Feng-Sheng Wang
  • Li-Hsunan Chen
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-9863-7_416


Particle swarm optimization is a heuristic algorithm that is somewhat similar to a genetic algorithm in that the system is initialized with a population of random solutions. Unlike other algorithms, however, each candidate solution (called a particle) is also assigned a randomized velocity and then flown through the problem hyperspace. Each particle searches for better positions in the search space by changing its velocity according to rules originally inspired by behavioral models of bird flocking. In this algorithm, a swarm of particles can move in the solution domain and search for the optimal solution over a spread region.



b = 1

For each particle i = 1, …,n

Initializw particle(i) with positionXiand velocityVi= 0

//randomly initiate n particles in the solution domain

If f(particle(i)) < f(particle(b)) than b = i

// find particle(b) as the best known solution

End for

Do until maximum iterations or particle(b) is good enough

For each particle i =...

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  1. Xu R, Wunsch DC II, Frank R (2007) Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans Comput Biol Bioinform 4(4):681–692PubMedGoogle Scholar

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© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan