metabolic-multi-agent-optimizer

name

metabolic-multi-agent-optimizer

description

Metabolic Multi-Agent Optimizer (MMAO) - bio-inspired optimization with endogenous resource allocation. Each agent carries internal energy with private-public metabolic loop. Fitness improvements converted to metabolic gains regulating sensing, search amplitude, branching, pruning, respawning. Parameter-light, self-calibrating. Use when: optimization without manual hyperparameter tuning, bio-inspired meta-heuristics, adaptive resource allocation in multi-agent systems.

Core Methodology

Metabolic Resource Loop

Each agent maintains:

• Internal energy (private metabolic state)
• Continuous role state (search behavior identity)
• Motion or structural memory
• Local search history

Population shares:

• Communal resource pool (public metabolic state)

Closed-Loop Control

Fitness improvement → Normalized metabolic gain → Energy pool → Regulates:
  ├── Sensing intensity
  ├── Search amplitude
  ├── Role drift
  ├── Branching (replication)
  ├── Pruning (death)
  ├── Respawning (birth)
  └── Elite reinvestment

Continuous Setting

• Energy-regulated symmetric zero-order probing
• Role-interpolated motion between agent types

Discrete Setting

• Structural sensing
• Local route improvement
• Guided perturbation
• Energy-weighted edge reuse

Key Properties

1. Endogenous: Adaptation derived from internal metabolic loop, not externally attached modules
2. Parameter-light: Self-calibrating through metabolic feedback
3. Cross-domain: Same control law works for continuous and discrete optimization
4. Validated: CEC2017 (10D/30D, 20 seeds) + TSPLIB (5 instances, 100 runs)

Progress Scale

Fitness improvements converted via:

• Robust progress scale (handles noisy improvements)
• Recent success statistic (short-term performance memory)

Implementation Pattern

class MetabolicAgent:
    energy: float  # Internal metabolic state
    role: float    # Continuous search behavior
    memory: dict   # Local search history
    
    def probe(self):
        amplitude = f(self.energy)  # Energy-regulated
        return symmetric_zero_order_probe(amplitude)
    
    def update_energy(self, fitness_delta):
        gain = robust_progress_scale(fitness_delta)
        self.energy += gain - metabolic_cost

When NOT to Use

• Not universally superior - main value is parameter-light self-calibration
• Use when manual hyperparameter tuning is costly, not when absolute performance is paramount

Activation

metabolic optimizer, bio-inspired optimization, multi-agent optimization, endogenous adaptation, parameter-light optimizer, MMAO, self-calibrating search, cs.NE