Particle Swarm Optimization [PSO]

Particle Swarm Optimization [PSO] is a powerful optimization algorithm inspired by the social behavior of birds flocking or fish schooling. It is known for its simplicity and effectiveness. PSO is widely used across various domains.

Key Terms/Concepts in PSO:

• Particles: Individual potential solutions in the swarm.
• Swarm: The entire group of particles searching for the optimal solution.
• Position: Represents a potential solution to the optimization problem.
• Velocity: The rate of change of the particle's position, guiding its movement through the solution space.
• Personal Best (pBest): The best solution a particle has found so far.
• Global Best (gBest): The best solution found by the entire swarm.
• Inertia Weight (ω): Controls the influence of the previous velocity, balancing exploration and exploitation.
• Cognitive Component (c1): Drives the particle towards its personal best position.
• Social Component (c2): Drives the particle towards the global best position.

Applications:

• Optimization problems like the Traveling Salesman Problem (TSP)
• Machine Learning
• Engineering design optimization
• Control systems

Advantages of PSO:

• Simple and easy to implement
• Efficient in finding optimal solutions
• Requires few parameters to adjust
• Can handle non-linear, multi-modal optimization problems