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Home/Authors/Jinliang Xu

Jinliang Xu

4 indexed papers

Recent (6 mo)
4
With code
0
Influential cites
0
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Publications per year

4
26

Top categories

Neural Computing×4Multiagent×3ML×1

Frequent co-authors

Liping Ma4×

Research Timeline

2026
MMAO: A Metabolic Multi-Agent Optimizer with Endogenous Resource Allocation for Continuous and Discrete Optimization

This paper introduces the Metabolic Multi-Agent Optimizer (MMAO), a self-calibrating optimization framework with endogenous resource allocation for continuous and discrete problems.

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

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.

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

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.

Mechanism and Stability Analysis of Metabolic Closed-Loop Metaheuristics

This paper analyzes the Metabolic Multi-Agent Optimizer (MMAO) framework at a high level, establishing properties and identifying behavioral regimes.

Highlighted terms show continued research focus across papers

Papers

cs.NEcs.MATheoreticalRecentJul 2, 2026

Mechanism and Stability Analysis of Metabolic Closed-Loop Metaheuristics

Jinliang Xu, Liping Ma

This paper analyzes the Metabolic Multi-Agent Optimizer (MMAO) framework at a high level, establishing properties and identifying behavioral regimes.

View →
cs.NEEmpiricalRecent
Jul 1, 2026

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

Jinliang Xu, Liping Ma

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.

View →
cs.NEcs.LGcs.MAEmpiricalRecentJul 1, 2026

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

Jinliang Xu, Liping Ma

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.

View →
cs.NEcs.MAEmpiricalRecentJun 26, 2026

MMAO: A Metabolic Multi-Agent Optimizer with Endogenous Resource Allocation for Continuous and Discrete Optimization

Jinliang Xu, Liping Ma

This paper introduces the Metabolic Multi-Agent Optimizer (MMAO), a self-calibrating optimization framework with endogenous resource allocation for continuous and discrete problems.

View →