Selected work

Representative Papers

MMAO-Dyn: A Metabolic Multi-Agent Optimizer for Dynamic Optimization

Jul 01, 2026

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.

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MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

Jul 01, 2026

This work addresses the joint optimization of feature selection and classifier hyperparameters for classification tasks by proposing MMAO-Cls, a method grounded in a metabolic multi-agent optimization framework. MMAO-Cls simultaneously encodes binary feature masks and hyperparameters within a mixed search space, incorporating an energy mechanism, population budgeting, and lifecycle modeling to balance accuracy and complexity. It innovatively leverages feature information priors to dynamically adjust the feature budget and introduces a regularized validation reward based on subset compactness and overfitting gap. Evaluated on seven standard tabular datasets, MMAO-Cls achieves an average test accuracy of 0.8882—outperforming RandomSearch and GA-lite—and yields the most compact feature subsets with an average feature ratio of 0.4881, although the performance gain does not reach statistical significance.

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Mechanism and Stability Analysis of Metabolic Closed-Loop Metaheuristics

Jul 01, 2026

This study investigates whether the resource cycling mechanism in Metabolic Multi-Agent Optimizers (MMAO) possesses framework-level explanatory power beyond metaphorical interpretation. By constructing an abstract state model that strips away domain-specific details while preserving the core resource accounting mechanism, the work leverages abstract modeling, dynamical analysis, and stability theory under mild bounded gain-and-expenditure assumptions to reveal, for the first time, the endogenous regulatory dynamics of MMAO at the architectural level. The analysis establishes the non-negativity and boundedness of key resource variables—such as private energy and public budget—and identifies three universal behavioral modes: contraction, reinvestment, and search reallocation. The generality of these modes is validated through both continuous and discrete instantiations, clearly distinguishing behaviors inherent to the metabolic feedback loop from those contingent on implementation specifics.

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A Large-Scale Empirical Evaluation of MMAO Under Fair-Budget Continuous and Discrete Benchmarks

Jun 30, 2026

This study systematically evaluates the closed-loop resource allocation mechanism of the Metabolic Multi-Agent Optimizer (MMAO) under a unified and strict budget constraint, demonstrating its effectiveness across both continuous and discrete optimization problems. Leveraging benchmark suites from CEC2017, TSPLIB, and OR-Library, the authors conduct large-scale empirical assessments against strong baselines—including PSO-lite, ES-lite, and iterative greedy 2-opt—to establish MMAO as the first cross-domain adaptive framework of its kind. Through trajectory-level diagnostics and ablation studies, the work reveals the robustness of MMAO’s endogenous resource reallocation capability. Results show that MMAO significantly outperforms baseline methods on both problem types, while ablated variants exhibit performance nearly matching the full model, confirming its ability to dynamically and efficiently allocate computational resources even under stringent budget limitations.

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Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search

Jun 29, 2026

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.

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Recent publications

Latest Papers

MMAO-Dyn: A Metabolic Multi-Agent Optimizer for Dynamic Optimization

Jul 01, 2026

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.

0 citationsRead paper

MMAO-Cls: Metabolic Multi-Agent Optimization for Joint Feature Selection and Classifier Tuning

Jul 01, 2026

This work addresses the joint optimization of feature selection and classifier hyperparameters for classification tasks by proposing MMAO-Cls, a method grounded in a metabolic multi-agent optimization framework. MMAO-Cls simultaneously encodes binary feature masks and hyperparameters within a mixed search space, incorporating an energy mechanism, population budgeting, and lifecycle modeling to balance accuracy and complexity. It innovatively leverages feature information priors to dynamically adjust the feature budget and introduces a regularized validation reward based on subset compactness and overfitting gap. Evaluated on seven standard tabular datasets, MMAO-Cls achieves an average test accuracy of 0.8882—outperforming RandomSearch and GA-lite—and yields the most compact feature subsets with an average feature ratio of 0.4881, although the performance gain does not reach statistical significance.

0 citationsRead paper

Mechanism and Stability Analysis of Metabolic Closed-Loop Metaheuristics

Jul 01, 2026

This study investigates whether the resource cycling mechanism in Metabolic Multi-Agent Optimizers (MMAO) possesses framework-level explanatory power beyond metaphorical interpretation. By constructing an abstract state model that strips away domain-specific details while preserving the core resource accounting mechanism, the work leverages abstract modeling, dynamical analysis, and stability theory under mild bounded gain-and-expenditure assumptions to reveal, for the first time, the endogenous regulatory dynamics of MMAO at the architectural level. The analysis establishes the non-negativity and boundedness of key resource variables—such as private energy and public budget—and identifies three universal behavioral modes: contraction, reinvestment, and search reallocation. The generality of these modes is validated through both continuous and discrete instantiations, clearly distinguishing behaviors inherent to the metabolic feedback loop from those contingent on implementation specifics.

0 citationsRead paper

A Large-Scale Empirical Evaluation of MMAO Under Fair-Budget Continuous and Discrete Benchmarks

Jun 30, 2026

This study systematically evaluates the closed-loop resource allocation mechanism of the Metabolic Multi-Agent Optimizer (MMAO) under a unified and strict budget constraint, demonstrating its effectiveness across both continuous and discrete optimization problems. Leveraging benchmark suites from CEC2017, TSPLIB, and OR-Library, the authors conduct large-scale empirical assessments against strong baselines—including PSO-lite, ES-lite, and iterative greedy 2-opt—to establish MMAO as the first cross-domain adaptive framework of its kind. Through trajectory-level diagnostics and ablation studies, the work reveals the robustness of MMAO’s endogenous resource reallocation capability. Results show that MMAO significantly outperforms baseline methods on both problem types, while ablated variants exhibit performance nearly matching the full model, confirming its ability to dynamically and efficiently allocate computational resources even under stringent budget limitations.

0 citationsRead paper

Minimal MMAO: A Resource-Closed-Loop Framework for Adaptive Metaheuristic Search

Jun 29, 2026

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.

0 citationsRead paper