robotic ants and bees are vacuuming up scrap metal and electronic junk

To achieve a swarm intelligent hive simulation:

1. Decentralized Control → Workers autonomously decide task assignments.
2. Stigmergy (Shared Knowledge) → Workers leave “digital pheromones” (metadata logs) to influence others.
3. Colony Optimization → Workers prioritize high-demand tasks based on feedback loops.
4. Worker Specialization → Drones self-optimize to become “specialists” in tasks they perform often.
5. Queen Coordination → The Queen AI oversees global trends but does not micromanage.

1️⃣ Ant Colony Optimization (ACO) 🐜

How it Works

• Workers leave digital pheromones on completed tasks (a database record or memory cache).
• Other workers choose tasks based on the strongest pheromone trails.
• Pheromone decay over time ensures old information does not mislead the hive.
• The Queen AI adjusts global pheromone strength for optimizing task allocation.

📌 Implementation Each worker logs task success rates and time-to-complete in a shared Redis cache:

import redis

r = redis.Redis(host="10.10.0.9", port=6379, decode_responses=True)

def leave_pheromone(task_id, success_rate):
    """Workers leave digital pheromones based on task success"""
    r.zincrby("pheromone_trails", success_rate, task_id)

def choose_task():
    """Workers choose tasks based on strongest pheromone trails"""
    task = r.zrevrange("pheromone_trails", 0, 0)  # Pick highest-ranked task
    return task[0] if task else None

✅ Result: Workers naturally specialize in tasks they excel at, and frequently needed tasks get prioritized.

2️⃣ Particle Swarm Optimization (PSO) 🌀

How it Works

• Each worker AI is a particle in a swarm.
• Particles adjust behavior based on:
  • Personal Best Performance (Worker tracks its past efficiency on tasks).
  • Global Best Performance (Queen AI tells workers which task solutions are performing best overall).
• Workers share knowledge dynamically and adjust strategies.

📌 Implementation Each worker AI evaluates its own task efficiency and compares with global best from Queen AI.

def update_worker_behavior(worker_id, task_success_rate):
    """Each worker adapts based on personal & global performance"""
    personal_best = r.get(f"worker:{worker_id}:best") or 0
    global_best = r.get("global_best") or 0

    # If this worker did better than its past performance, update personal best
    if task_success_rate > float(personal_best):
        r.set(f"worker:{worker_id}:best", task_success_rate)

    # If this is better than any other worker, update global best
    if task_success_rate > float(global_best):
        r.set("global_best", task_success_rate)

✅ Result: Workers learn over time which task strategies work best and converge toward global optimal behavior.

3️⃣ Stochastic Diffusion Search (SDS) 🔄

How it Works

• Instead of all workers processing separate tasks, workers communicate.
• If a worker finds a good solution, it tells other workers to replicate or refine it.
• If a worker fails at a task, it asks neighboring workers for guidance.

📌 Implementation Workers randomly check if their peers have found a better way to complete a task:

import random

def consult_neighbors(worker_id, task_id):
    """Workers communicate with each other to find better strategies"""
    neighbors = ["worker1", "worker2", "worker3"]  # Example neighbors
    chosen_neighbor = random.choice(neighbors)

    best_known_method = r.get(f"worker:{chosen_neighbor}:task:{task_id}")
    if best_known_method:
        return best_known_method  # Worker adopts the better method
    return None

✅ Result: Workers dynamically learn from each other, allowing rapid knowledge transfer.

4️⃣ Bee Colony Algorithm 🐝

How it Works

• Workers are divided into Explorers and Foragers:
  • Explorers try new tasks and see if they are efficient.
  • Foragers do proven tasks that maximize efficiency.
• If an Explorer finds a good task, more Foragers will switch to it.

📌 Implementation Each worker has a chance to explore or exploit:

import random

def decide_role():
    """Workers randomly decide if they should explore or exploit"""
    return "Explorer" if random.random() < 0.3 else "Forager"

✅ Result: Workers balance exploration of new AI models vs. exploiting well-performing ones.

📌 Implementing the Hive-Mind in Your Cluster

1️⃣ Modify Task Assignment in Node9 (Coordinator)

Modify task routing so workers self-select their tasks instead of blindly accepting them.

📌 Enhanced Task Routing

def distribute_task(question):
    """Workers self-select tasks based on pheromone strength"""
    best_task = choose_task()  # Selects task using Ant Colony Optimization

    if best_task:
        worker_node = get_least_busy_worker()
        channel.basic_publish(exchange="", routing_key="task_queue", body=json.dumps({"question": best_task, "worker": worker_node}))
        print(f"[Coordinator] Assigned best-fit task to worker {worker_node}")
    else:
        # No optimized task found, send to Queen AI
        channel.basic_publish(exchange="", routing_key="queen_queue", body=json.dumps({"question": question}))
        print("[Coordinator] Assigned to Queen AI.")

✅ Result: Tasks are dynamically chosen by worker AI preference, not just brute-force assignment.

2️⃣ Implement Worker Specialization (Adaptive AI Roles)

Each worker tracks its best task and requests similar work.

📌 Worker Self-Specialization

def request_task(worker_id):
    """Workers request tasks they are good at"""
    best_task = r.get(f"worker:{worker_id}:best_task")
    if best_task:
        return best_task
    return choose_task()  # Fallback to colony-wide best task

✅ Result: AI workers develop specialties, just like in real hives.

🔥 Final Architecture

• Node1 & Node2 (Queen AI) → DeepSeek-R1:8B for high-level reasoning.
• Nodes 1, 2, 3 (Workers) → Small AI models for distributed inference.
• Worker AI uses Swarm Intelligence:
  • 🐜 Ant Colony Optimization for task selection.
  • 🌀 Particle Swarm Optimization for dynamic learning.
  • 🔄 Stochastic Diffusion Search for knowledge transfer.
  • 🐝 Bee Algorithm for balancing exploration/exploitation.