A Review of Particle Swarm Optimization

Authors

  • Anshika Singh
  • Sumit Mathur

DOI:

https://doi.org/10.37591/joces.v11i2.850

Keywords:

PSO, constraint optimization problem, multi-objective optimization

Abstract

Particle swam optimization (PSO) is a computational technique that streamlines a problem by attempting to improve the arrangement of an applicant with respect to a given value ratio iteratively. This approaches the problem by having a number of candidate solutions, and then pushes the particles in the search space according to a mathematical equation over the position and velocity of the particles. Researchers and specialists have provided various perspectives and studies on Particle Swam Optimization approaches and with the assistance of certain PSO approaches they have effectively answered numerous genuine questions. A summary of certain versions of PSO has been provided in this paper.

References

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Chunxia F, Youhong W. An adaptive simple particle swarm optimization algorithm 2008. Chinese control and decision conference. Yantai, Shandong, 2008, pp. 3067–72.

Munlin M, Anantathanavit M. New social-based radius particle swarm optimization. 12th IEEE Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, 2017; 2017. p. 838843.

Alhussein M, Haider SI. Improved particle swarm optimization based on velocity clamping and particle penalization. 3rd International Conference on Artificial Intelligence. Kota Kinabalu: Modelling and Simulation (AIMS); 2015. p. 61–4.

Peng H, Deng C. Dynamic neighborhood hybrid particle swarm optimization for constrained optimization. International Conference on Computational and Information Sciences, Chengdu, 2010; 2010. p. 1126–9.

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Published

2021-12-01

Issue

Vol. 11 No. 2 (2021)

Section

Articles

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