Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search
2026-06-29 • Neural and Evolutionary Computing
Neural and Evolutionary ComputingMultiagent Systems
AI summaryⓘ
The authors introduce the Metabolic Multi-Agent Optimizer (MMAO), a new approach where agents share a common resource system to guide their search efforts in problem-solving. Instead of fixed rules for exploring solutions or deciding when to stop, MMAO uses a shared energy budget that controls how agents adapt, take roles, and manage their populations. They tested MMAO on different types of problems, including continuous and discrete challenges, and found it works consistently well without needing many extra parts. Overall, the authors suggest MMAO offers a unified way to design adaptive search methods rather than combining unrelated techniques.
metaheuristicmulti-agent systemresource circulationexploration-exploitationoptimizationcontinuous domaindiscrete domaintraveling salesman problemadaptive heuristicspopulation control
Authors
Jinliang Xu, Liping Ma
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.