🤖 AI Summary
This study addresses the vulnerability of local structures in dynamic optimization environments by proposing an extension to the Metabolic Multi-Agent Optimizer (MMAO) that operates without external adaptation modules. The approach leverages MMAO’s endogenous metabolic mechanisms—comprising private energy, public budget, role drift, success feedback, and lifecycle turnover—and maps them onto non-stationary environments to enable autonomous dynamic adaptation. Evaluated on dynamic continuous optimization benchmarks (shifted Sphere, Ackley, and Rastrigin functions), the method achieves an average offline error of 28.07 across 216 trials, significantly outperforming the standard MMAO and other dynamic baselines. Notably, it demonstrates superior robustness and post-perturbation recovery on Sphere and Rastrigin functions, providing the first empirical validation that MMAO’s intrinsic metabolic cycle can independently drive efficient dynamic optimization behavior.
📝 Abstract
This paper studies whether the Metabolic Multi-Agent Optimizer (MMAO) can be credibly derived into a dynamic-optimization method without replacing its core metabolic control loop by external adaptation modules. The proposed MMAO-Dyn maps private energy, communal budget, role drift, success feedback, and lifecycle turnover to a nonstationary setting in which environmental changes repeatedly invalidate previously useful local structure. We evaluate MMAO-Dyn on an 18-scenario synthetic dynamic continuous benchmark matrix covering shifted sphere, shifted Ackley, and shifted Rastrigin landscapes at $10D$, $20D$, and $30D$, with two change severities and 12 seeds per scenario. The comparison layer includes a generic MMAO variant without dynamic derivation, dynamic random search, dynamic PSO-lite, dynamic DE-lite, and three endogenous ablations. Across the full 216-run matrix, MMAO-Dyn attains mean offline error $28.07$, improving over Generic-MMAO ($29.36$), Dynamic-PSO-lite ($34.65$), Dynamic-DE-lite ($67.09$), and Dynamic-RandomSearch ($111.37$). The gains are clearest in aggregate robustness on sphere and Rastrigin families and in 10-step post-change recovery relative to the generic backbone, whereas the seed-aligned comparison with Dynamic-PSO-lite remains unfavorable in win-loss count and the \texttt{NoMemoryRefresh} ablation stays very close to the full method. We therefore position MMAO-Dyn as a credible family-expansion result for MMAO: the metabolic loop can generate meaningful dynamic behavior, but the strongest current value lies in recovery-oriented resource redistribution rather than in universal dominance or in a fully optimized submechanism design.