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CNRS

Academic institutioneurope · fr
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Research library2,892linked papers
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Selected work

Representative Papers

Monte Carlo Graph Coloring

Apr 04, 2025Communications in Computer and Information Science

This work addresses the NP-hard large-scale graph coloring problem by systematically adapting Monte Carlo search (MCS) paradigms—specifically Nested Monte Carlo Search (NMCS) and Nested Rollout Policy Adaptation (NRPA)—to this classical combinatorial optimization task for the first time. We propose a graph-coloring-specific state representation, a structured action space modeling, and a conflict-aware heuristic rollout policy, integrated with greedy initialization, dynamic vertex ordering, and conflict-driven backtracking. Experimental evaluation on standard benchmark graphs demonstrates that our approach improves coloring quality by 12–23% over mainstream heuristics—including DSATUR, RLF, and TABUCOL—on instances with over 100 vertices. Moreover, it achieves superior computational efficiency compared to most metaheuristics and significantly surpasses the scalability limits of traditional exact algorithms.

18 citationsRead paper

Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments (Extended Abstract)

Feb 16, 2022Description Logics

This paper addresses inconsistent-tolerant query answering over prioritized knowledge bases—comprising logical theories, factual databases, and priority relations among conflicting facts. We systematically support query evaluation under three classical semantics—AR (cautious), IAR (intersection of all repairs), and brave—over two classes of optimal repair models: Pareto-optimal and completion-based repairs. Our key contribution is the first unified SAT encoding framework capable of handling arbitrary priority relations, enabling joint modeling and efficient solving for both repair classes and all three semantics. Based on this encoding, we implement ORBITS, a novel reasoning system. Experimental results demonstrate that ORBITS significantly outperforms baseline approaches across all semantics, highlighting the critical impact of semantic choice and solving strategy on performance. The work establishes a new paradigm for practical reasoning over inconsistent prioritized knowledge bases.

18 citationsRead paper

MSA-technique for stiffness modeling of manipulators with complex and hybrid structures

Nov 19, 2025IFAC Symposium on Robot Control

Traditional stiffness modeling methods struggle to simultaneously achieve accuracy, computational efficiency, and topological adaptability for complex hybrid-configured robotic manipulators featuring closed-loop kinematics, flexible links, rigid/elastic joints, and coupled preload–external load conditions. To address this, this paper proposes a Modular Stiffness Analysis (MSA) framework that integrates matrix structural analysis, screw theory, and finite-element discretization. It is the first work to systematically introduce a modular strategy into stiffness modeling, enabling flexible composition and rapid analytical derivation for diverse configurations—including rigid–flexible coupling and parallel topologies. The resulting global stiffness matrix achieves over 30% higher computational efficiency compared to conventional approaches, with modeling errors bounded by ≤5%. Comprehensive validation across multiple representative hybrid-architecture manipulators demonstrates both high accuracy and strong generalizability.

12 citationsRead paper

Bayesian optimization for mixed variables using an adaptive dimension reduction process: applications to aircraft design

Apr 11, 2025

In multidisciplinary design optimization—particularly aircraft design—the presence of high-dimensional mixed variables (continuous, integer, and categorical) causes combinatorial explosion in the hyperparameter space of Bayesian optimization surrogate models. To address this, we propose a Partial Least Squares (PLS)-based adaptive dimensionality reduction framework. It dynamically learns variable coupling structures to significantly compress the surrogate hyperparameter space, while integrating mixed-variable encoding with an adaptive hyperparameter selection mechanism to balance modeling accuracy and tuning efficiency. Evaluated on analytical benchmarks and two real-world aircraft design cases, our method achieves >30% faster convergence and improves optimal solution quality by 12–18% compared to genetic algorithms, while reducing hyperparameter count by ~60%. Our core contribution is the first application of PLS for hyperparameter pruning in Bayesian optimization, enabling efficient and robust optimization under high-dimensional mixed-variable settings.

8 citations3 influentialRead paper

Constrained Multi-Objective Bayesian Optimization with Application to Aircraft Design

Jun 20, 2022AIAA AVIATION 2022 Forum

Bayesian optimization (BO) methods for computationally expensive, nonlinearly constrained multi-objective optimization problems—such as aircraft conceptual design—often suffer from ill-conditioning in multi-objective acquisition functions, leading to unstable surrogate updates and poor convergence. Method: This paper extends the SEGOMOE framework by introducing a novel regularization mechanism directly into the multi-objective acquisition function, synergistically integrating Kriging surrogates, a Mixture-of-Experts (MoE) architecture, and an enhanced SEGO algorithm. Contribution/Results: The proposed approach systematically alleviates the trade-off between ill-conditioning and convergence in constrained multi-objective BO. Empirical evaluation on aircraft design tasks demonstrates that it achieves high-quality Pareto fronts using only 5% of the function evaluations required by NSGA-II, significantly improving the efficiency of identifying high-fidelity, low-cost compromise solutions.

8 citationsRead paper
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