Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search

Jinliang Xu; Liping Ma

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arXiv · 2606.30450

Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search

Jinliang Xu·Liping Ma

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Abstract

This paper presents the Metabolic Multi-Agent Optimizer (MMAO) as an adaptive metaheuristic built around endogenous resource circulation. The central premise is that search intensity, exploration--exploitation balance, and lifecycle turnover should be induced by a shared metabolic controller rather than by separately attached schedules. We formulate MMAO through bounded private energy, a communal budget, normalized reward, continuous role adaptation, and resource-financed branching and pruning. The method is then instantiated in both continuous and discrete domains and evaluated on a matched small-scale suite including Sphere, Rastrigin, a synthetic Euclidean TSP, and two TSPLIB instances. The results show a consistent pattern: the same metabolic loop remains workable across domains, the discrete realization remains relatively stable under a compact design, and continuous refinement quality is the main cost of keeping the method lean. Taken together, these findings position MMAO as a coherent framework for adaptive heuristic design rather than a loose collection of operators.

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Jinliang Xu, Liping Ma. 2026-06-29. Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search. https://arxiv.org/abs/2606.30450

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