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Particle Swarm Optimization: Definition Presentation

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Updated: Jun 10th, 2022

What is Particle Swarm Optimization?

  • Particle swarm optimization (PSO) is an artificial intelligence (AI) or a computational method for getting solutions to problems through maximization and minimization of numeric.
  • Emanated from social behavior of bird flocking or fish schooling.
  • Particles are the population of candidate solutions that move around in a search-space.
  • Particles move within the search-space and therefore have velocity.
  • Particles also move to find their best known positions.
  • Particles move within the problem space by following the current optimum particles.
  • Swarm particles work together and exchange information about the best known solutions.
  • Particles rely on information within their neighborhood.
  • Particles understand conditions of others in the neighborhood.
  • The position of a particle with the best known fitness or solution guides other particles.
  • The position helps in optimizing the particle’s velocity.
What is Particle Swarm Optimization? What is Particle Swarm Optimization? What is Particle Swarm Optimization?

How is It Optimized?

  • Particles change their positions to new ones.
  • Particles move by changing their velocity.
  • Adjustment of velocity involves:
    • A n improvement in current velocity.
    • A focus in the direction that can offer the best solution.
    • A focus in the direction that can offer best solution.
  • The new velocity leads to the current position, which bears the old position and the new velocity.
  • A change in position depends on an individual’s comfort and what society considers ideal.
  • Particles always look for the best known solutions.
  • Optimization is an iterative process.
  • The PSO equation could look complex.
  • Particle’s current velocity updates depend on current velocity, information available and information received from the entire swarm.
  • The best available fitness or solution is the key focus of particles.
  • Velocities control the movement of the particles in the problem space.
How is It Optimized? How is It Optimized? How is It Optimized?

The Algorithm

  • Particles bear maximum velocity, Vmax in every dimension.
  • Acceleration could result in a great sum of velocity than the Vmax under the user defined parameter.
  • The velocity within the dimension could be limited to Vmax.
The Algorithm The Algorithm The Algorithm

Conclusion

  • The PSO is a simple algorithm for optimization.
  • It is has several functions.
  • Particles rely on two best values for updates, which are best values achieved so far and the best value achieved so far by any particle in the search-space.
Conclusion
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