Jinliang Xu
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This paper introduces the Metabolic Multi-Agent Optimizer (MMAO), a self-calibrating optimization framework with endogenous resource allocation for continuous and discrete problems.
This paper proposes MMAO-Dyn, a dynamic optimization method derived from Metabolic Multi-Agent Optimizer (MMAO), and evaluates its performance on a synthetic dynamic benchmark.
This paper proposes MMAO-Cls, a new approach for classification model selection using the Metabolic Multi-Agent Optimizer (MMAO), and compares it against other methods.
This paper analyzes the Metabolic Multi-Agent Optimizer (MMAO) framework at a high level, establishing properties and identifying behavioral regimes.