🤖 AI Summary
This work proposes a metabolic multi-agent optimization framework that addresses the limitations of traditional metaheuristics, which rely on fixed population sizes and manually tuned search parameters and thus lack intrinsic adaptability. The framework introduces, for the first time, an endogenous metabolic resource allocation mechanism into optimization algorithms: agents carry energy, role states, and memory, enabling self-calibrating collaborative search through private–public metabolic resource cycles. In continuous domains, it employs energy-regulated symmetric zeroth-order probing and role-interpolated movement; in discrete domains, it integrates structure-aware guided perturbations with energy-weighted edge reuse. Experimental results demonstrate that this lightweight, low-parameter approach effectively achieves heterogeneous, self-adaptive cooperative search behavior without external parameter tuning, as validated on CEC2017 (10D/30D) and TSPLIB benchmarks.
📝 Abstract
Traditional meta-heuristics often rely on fixed population sizes, manually chosen search scales, and externally attached parameter-control modules. This paper presents the \textit{Metabolic Multi-Agent Optimizer} (MMAO), a cross-domain optimization framework in which adaptation is derived endogenously from a private-public metabolic resource 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 through a robust progress scale and a recent success statistic; the same closed loop then regulates sensing intensity, search amplitude, role drift, branching, pruning, respawning, and elite reinvestment. In the continuous setting, MMAO uses energy-regulated symmetric zero-order probing and role-interpolated motion. In the discrete setting, the same control law is instantiated through structural sensing, local route improvement, guided perturbation, and energy-weighted edge reuse. The paper combines an implementation-faithful formulation with a reproducible experimental study on a CEC2017 subset (10D/30D, 20 seeds) and five TSPLIB instances (100 discrete runs in total). The current evidence supports MMAO primarily as a parameter-light, self-calibrating optimization framework whose main validated originality lies in metabolically endogenous resource allocation across heterogeneous search behaviors, rather than as a universally superior optimizer.