Kuramoto Multi-Objective Optimization
Author: Alireza Fouladgar
This project implements a multi-objective optimization pipeline for finding the best network topology for Kuramoto oscillators using a hybrid approach:
Genetic Algorithm (GA) Particle Swarm Optimization (PSO) Local Hill-Climbing Robustness-aware Kuramoto simulation
This notebook optimizes a network of Kuramoto oscillators for synchrony and robustness to noise. It uses Genetic Algorithm (GA), Hill-Climbing, Particle Swarm Optimization (PSO), and a Hybrid GA+PSO+Hill method.
How to run
- Open the notebook:
jupyter notebook kuramoto_multiobjective.ipynb - Run all cells to see results and plots.