Particle Swarm Optimization: Definition

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.

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.

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.

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.