This document introduces Particle Swarm Optimization (PSO), inspired by social behaviors of birds and fish searching for food. It describes how individual solutions (particles) evaluate their performance based on a fitness value and adjust their movement by considering both individual and collective best performances. Key elements of the PSO include parameters like swarm size, particle position, velocity, and control parameters to optimize the search for the best solution.

1. Introduction to Particle
Swarm Optimization

2. • Inspired by social behavior of bird flocking and fish schooling
• So what is the best strategy to locate the food?
• Suppose a group of birds is searching food in an area
• Only one piece of food is available
• Birds do not have any knowledge about the location of the food
• But they know how far the food is from their present location
• The best strategy is to follow the bird nearest to the food

5. Particle Swarm Algorithm
• Each solution is considered as bird, called particle
• All the particles have a fitness value. The fitness values can
• be calculated using objective function
• All the particles preserve their individual best performance
• They also know the best performance of their group
• They adjust their velocity considering their best performance and also
• considering the best performance of the best particle
• 1. Evaluate
• ○ Quantifies the goodness of each particle
• 2. Compare
• ○ Obtains the best solution by comparing the particles
• 3. Imitate

9. Parameters and its importance
• Initial Parameters
• Swarm size
• Position f particles
• Velocity of the particles
• Maximum number of iteration
• Control parameters
• Swarm size
• Inertial weight
• Acceleration coefficients
• Number of iterations

11. • Numerical example
• https://www.youtube.com/watch?v=uwXFnzWaCY0