https://github.com/2gpb/sa-particleswarmoptimization
This repository contains an implementation of the Self-Adaptive Particle Swarm Optimization (SA-PSO) algorithm, a dynamic method that adjusts its parameters to optimize problem-solving.
https://github.com/2gpb/sa-particleswarmoptimization
nature-inspired optimization-algorithm python
Last synced: 2 months ago
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This repository contains an implementation of the Self-Adaptive Particle Swarm Optimization (SA-PSO) algorithm, a dynamic method that adjusts its parameters to optimize problem-solving.
- Host: GitHub
- URL: https://github.com/2gpb/sa-particleswarmoptimization
- Owner: 2Gpb
- Created: 2025-02-06T12:22:54.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-03-16T12:53:47.000Z (3 months ago)
- Last Synced: 2025-03-16T13:33:39.976Z (3 months ago)
- Topics: nature-inspired, optimization-algorithm, python
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SelfAdaptive-ParticleSwarmOptimization (SA-PSO)
This project implements the Self-Adaptive Particle Swarm Optimization (SA-PSO) algorithm, which combines the concepts of Particle Swarm Optimization (PSO) and Self-Adaptive mechanisms. The main feature of SA-PSO is the dynamic adaptation of the inertia weight w during the optimization process, improving the balance between exploration and exploitation.
### Main Components
- **Particle Class** — Represents an individual particle with methods to update its velocity, position, and track the best position based on fitness.
- **SAParticleSwarmOptimization Class** — Manages the swarm, dynamically adjusts the inertia weight, and iteratively updates particles’ positions and velocities to find an optimal solution.
### Getting Started:
To run this project, you need to install the required dependencies. You can install them using pip:
```
pip install -r requirements.txt
```
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