Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search
For researchers in metaheuristic optimization, this work proposes a unified framework for adaptive search, but its incremental nature and limited benchmarking (small-scale problems) suggest modest immediate impact.
The paper introduces the Metabolic Multi-Agent Optimizer (MMAO), a metaheuristic framework where search dynamics are governed by a shared metabolic controller instead of separate schedules. Evaluated on continuous and discrete optimization problems, MMAO demonstrates consistent performance across domains with a compact design, though continuous refinement quality is a trade-off for simplicity.
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.