---
weight: 300
title: "PSO"
description: "Particle Swarm Optimization for Multi-Objective Community Detection"
icon: "scatter_plot"
lead: ""
date: 2025-11-20T00:00:00+00:00
lastmod: 2025-11-20T00:00:00+00:00
draft: false
images: []
---

{{% alert context="warning" text="**Coming Soon**: The PSO algorithm is currently under development. This documentation serves as a placeholder for the upcoming release." /%}}

The Particle Swarm Optimization (PSO) algorithm for community detection is a swarm intelligence approach that uses a population of particles to collaboratively explore the solution space. Each particle represents a potential community structure and moves through the solution space guided by its own experience and the swarm's collective knowledge.

PSO will be implemented in [Rust](https://www.rust-lang.org/) for performance and exposed to Python via [PyO3](https://pyo3.rs/v0.24.0/), matching the architecture of HP-MOCD.

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## Planned Features

- **Swarm Intelligence**: Population-based optimization with social learning
- **Multi-Objective**: Simultaneous optimization of multiple community quality metrics
- **Parallel Architecture**: Fully parallelized Rust implementation
- **Velocity-Based Exploration**: Adaptive search guided by inertia, cognitive, and social components
- **Fast Convergence**: Rapid approach to near-optimal solutions

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**Status**: 🚧 Under Development

Check back soon for complete documentation, or explore [HP-MOCD](/docs/algorithms/hpmocd/) which is fully implemented and ready to use.
