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Example: Creating and Running a Swarm Optimization

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Example: Creating and Running a Swarm Optimization

# 1. Start the Julia server in one terminal
cd julia
julia --project=. julia_server.jl

# 2. Run the interactive CLI in another terminal
node packages/cli/interactive.cjs

# 3. From the interactive menu:
# - Select "🧬 Swarm Intelligence"
# - Select "Create Swarm"
# - Enter a name for your swarm (e.g., "OptimizationSwarm")
# - Select an algorithm (e.g., "DE" for Differential Evolution)
# - Enter swarm configuration as JSON (can use {} for defaults)
# - Select "Run Optimization"
# - Define your objective function (e.g., "function(x) return x[1]^2 + x[2]^2 end")
# - Enter optimization parameters (bounds, population size, etc.)
# - View the results when optimization completes

Alternatively, you can use the Python wrapper:

import asyncio
from juliaos import JuliaOS

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

    # Create a swarm
    swarm = await juliaos_client.swarms.create_swarm(
        name="OptimizationSwarm",
        algorithm="DE",
        config={
            "population_size": 50,
            "crossover_rate": 0.8,
            "mutation_factor": 0.5
        }
    )

    # Run optimization
    result = await juliaos_client.swarms.run_optimization(
        swarm_id=swarm["id"],
        objective_function="function(x) return sum(x.^2) end",
        parameters={
            "bounds": [(-10, 10), (-10, 10), (-10, 10)],
            "max_iterations": 100
        }
    )

    print(f"Best position: {result['best_position']}")
    print(f"Best fitness: {result['best_fitness']}")

    await juliaos_client.disconnect()

asyncio.run(run_optimization())
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