🤖 AI Summary
This work proposes a metabolic multi-agent optimization framework grounded in endogenous resource cycling, addressing the limited intrinsic adaptability of traditional metaheuristic algorithms that rely on external scheduling mechanisms. The framework employs a shared metabolic controller to dynamically regulate agent lifecycles, role transitions, and resource allocation, thereby establishing a lightweight, self-consistent closed-loop resource system. It unifies energy budgeting, normalized reward signals, and continuous role adaptation, enabling effective handling of both continuous and discrete optimization problems. Empirical evaluations on benchmark functions—including Sphere and Rastrigin—as well as synthetic and TSPLIB traveling salesman problem instances demonstrate the method’s cross-domain stability and computational efficiency, highlighting its compact architecture and general-purpose adaptive capabilities.
📝 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.