MMAO: New Metabolic Multi-Agent Optimizer Self-Calibrates Without Manual Tuning

Researchers Jinliang Xu and Liping Ma have introduced MMAO, a Metabolic Multi-Agent Optimizer that replaces manual parameter tuning with an endogenous resource allocation loop. Each agent carries internal energy, a continuous role state, motion or structural memory, and local search history, while the population shares a communal resource pool. Fitness improvements are converted into normalized metabolic gains that regulate sensing intensity, search amplitude, role drift, branching, pruning, respawning, and elite reinvestment. In continuous optimization, MMAO uses energy-regulated symmetric zero-order probing and role-interpolated motion; for discrete problems, the same control law is instantiated through structural sensing, local route improvement, guided perturbation, and energy-weighted edge reuse.

The paper validates MMAO on a CEC2017 subset (10D/30D, 20 seeds) and five TSPLIB instances (100 discrete runs total). The authors position MMAO as a parameter-light, self-calibrating framework whose main originality lies in metabolically endogenous resource allocation across heterogeneous search behaviors—not as a universally superior optimizer. This metabolic approach could inspire a new class of optimization algorithms that adapt autonomously to problem landscapes, reducing the need for expert tuning in complex engineering, logistics, and machine learning tasks.