Torchswarm

A fast implementation of Particle Swarm Optimization using PyTorch

We support

Variants of Particle Swarm Optimization

We support for all kinds of PSO

Bring your own particle

We allow for getting a custom particle with a different velocity update rule, The Class must have the following methods:

• __init__
• move
• update_velocity

How to define your problem.

Create a class by inheriting torchswarm.functions.Function and an evaluate method.

class XSquare(Function):
    def evaluate(self, x):
        return x**2

Example

import torch

from torchswarm.swarmoptimizer import SwarmOptimizer


class CubicFunction:
    def evaluate(self, x):
        return x ** 2 + torch.exp(x)

empso = SwarmOptimizer(1, 100, swarm_optimizer_type="exponentially_weighted", max_iterations=10)
empso.optimize(CubicFunction())

print(empso.run(verbosity=True).__dict__)

Contributors:

• Rohan Mohapatra