How do I implement Particle Swarm Optimization (PSO)?

How do I implement Particle Swarm Optimization (PSO) in Python?

To implement PSO in Python, you can start by defining a particle class that includes position, velocity, and fitness.

What are the key parameters I need to set for PSO?

The key parameters include the number of particles, cognitive and social coefficients, and the maximum number of iterations.

Can you provide a simple code example?

Sure! Here's a basic example:

import numpy as np

class Particle:
    def __init__(self, bounds):
        self.position = np.random.uniform(bounds[0], bounds[1])
        self.velocity = np.random.uniform(-1, 1)
        self.best_position = self.position
        self.best_value = float('inf')

# Initialize particles and implement PSO logic here.

This is just a starting point!

How do I evaluate the fitness of each particle?

You can define a fitness function that takes a particle's position as input and returns a fitness value based on your optimization goal.