Example: Using the Benchmarking Feature

JuliaOS includes a comprehensive benchmarking suite for evaluating and comparing swarm optimization algorithms. This feature helps you select the most appropriate algorithm for your specific optimization problems.

# Start the CLI
./scripts/run-cli.sh  # or node packages/cli/interactive.cjs

# Select "🧬 Swarm Intelligence" from the main menu
# Choose "📊 Benchmark Algorithms"
# Select the algorithms to benchmark (e.g., DE, PSO, GWO, DEPSO)
# Choose the benchmark functions (e.g., Sphere, Rastrigin, Rosenbrock, Ackley, Griewank)
# Set the benchmark parameters (dimensions, runs, etc.)
# Run the benchmark and view the results

The benchmarking CLI provides an interactive interface for:

• Selecting algorithms to benchmark (DE, PSO, GWO, ACO, GA, WOA, DEPSO)

• Choosing benchmark functions with different difficulty levels

• Setting dimensions, runs, and evaluation limits

• Comparing algorithm performance across different metrics

• Generating comprehensive HTML reports with visualizations

• Ranking algorithms based on performance metrics

You can also use the Python wrapper to access the benchmarking functionality:

import asyncio
from juliaos import JuliaOS

async def run_benchmark():
    # Initialize JuliaOS
    juliaos_client = JuliaOS(host="localhost", port=8052)
    await juliaos_client.connect()

    # Run benchmark
    result = await juliaos_client.swarms.run_benchmark(
        algorithms=["DE", "PSO", "GWO", "DEPSO"],
        functions=["sphere", "rastrigin", "rosenbrock", "ackley", "griewank"],
        dimensions=10,
        runs=10,
        max_iterations=1000,
        population_size=50
    )

    # Print results
    print("Benchmark Results:")
    for func_name, func_results in result.items():
        print(f"\nFunction: {func_name}")
        for algo, metrics in func_results.items():
            print(f"  Algorithm: {algo}")
            print(f"    Mean Best Fitness: {metrics['mean_best_fitness']}")
            print(f"    Success Rate: {metrics['success_rate']}")
            print(f"    Mean Iterations: {metrics['mean_iterations']}")
            print(f"    Mean Time: {metrics['mean_time']} seconds")

    # Generate visualization
    visualization = await juliaos_client.swarms.generate_benchmark_visualization(
        benchmark_results=result,
        visualization_type="convergence"
    )

    # Save visualization to file
    with open("benchmark_visualization.html", "w") as f:
        f.write(visualization)

    print("\nVisualization saved to benchmark_visualization.html")

    await juliaos_client.disconnect()

# Run the benchmark
asyncio.run(run_benchmark())

The benchmarking feature provides:

• Comparison of multiple swarm algorithms on standard test functions

• Performance metrics including success rate, convergence speed, and solution quality

• Statistical analysis of algorithm performance across multiple runs

• Visualization of convergence behavior and performance comparisons

• Parameter sensitivity analysis to optimize algorithm settings

• Export of results in various formats (CSV, JSON, HTML)