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Particle Swarm Optimization

Authors: James Kennedy, Russell C. Eberhart

Published: 1995 (Conference Paper)

Source: International Conference on Neural Networks (ICNN)

Algorithm: Particle Swarm Optimization

DOI: 10.1109/ICNN.1995.488968

Summary

Introduces Particle Swarm Optimization, a population-based optimizer inspired by simplified social and flocking behavior. The paper establishes the particle position and velocity update metaphor and positions PSO as an alternative to genetic algorithms for nonlinear function optimization and neural-network training.

Abstract

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.

Primary

Paper doi.org

Standard

DOI 10.1109/ICNN.1995.488968

Alternate

ieeexplore.ieee.org ieeexplore.ieee.org explore.openaire.eu explore.openaire.eu

Tags

  • Particle swarm optimization

  • Swarm intelligence

  • Evolutionary computation

  • Metaheuristics

  • Nonlinear optimization

  • Neural network training

  • Artificial life