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Preference optimization via Contrastive Divergence: Your Reward Model is Secretly an NLL Estimator

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arXiv 2025年

作者： Chen, Zhuotong Liu, Fang Zhu, Xuan Wang, Haozhu Qi, Yanjun Ghavamzadeh, Mohammad Amazon Web Services Santa ClaraCA United States SunnyvaleCA United States

Existing studies on preference optimization (PO) have centered on constructing pairwise preference data following simple heuristics, such as maximizing the margin between preferred and dispreferred completions based on human (or AI) ranked scores. However, none of these heuristics has a full theoretical justification. In this work, we develop a novel PO framework that provides theoretical guidance to effectively sample dispreferred completions. To achieve this, we formulate PO as minimizing the negative log-likelihood (NLL) of a probability model and propose to estimate its normalization constant via a sampling strategy. As we will demonstrate, these estimative samples can act as dispreferred completions in PO. We then select contrastive divergence (CD) as the sampling strategy, and propose a novel MC-PO algorithm that applies the Monte Carlo (MC) kernel from CD to sample hard negatives w.r.t. the parameterized reward model. Finally, we propose the OnMC-PO algorithm, an extension of MC-PO to the online setting. On popular alignment benchmarks, MC-PO outperforms existing SOTA baselines, and OnMC-PO leads to further improvement. © 2025, CC BY.

关键词： optimization algorithms

Optimal reshaping and stress controlling of double-layer spherical structures under vertical loadings

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ARCHIVES OF CIVIL ENGINEERING 2022年第4期68卷 591-606页

作者： Manguri, Ahmed Saeed, Najmadeen Mahmood, Aram Katebi, Javad Jankowski, Robert Gdansk Univ Technol Fac Civil & Environm Engn PL-80223 Gdansk Poland Univ Raparin Civil Engn Dept Rania Kurdistan Regio Iraq Tishk Int Univ Civil Engn Dept Erbil Kurdistan Regio Iraq Univ Tabriz Fac Civil Engn Tabriz Iran

Architectural structures' nodal coordinates are significant to shape appearance;vertical overloading causes displacement of the joints resulting in shape distortion. This research aims to reshape the distorted shape of a double-layer spherical numerical model under vertical loadings;meanwhile, the stress in members is kept within the elastic range. Furthermore, an algorithm is designed using the fmincon function to implement as few possible actuators as possible to alter the length of the most active bars. Fmincon function relies on four optimization algorithms: trust-region reflective, active set, Sequential quadratic progra mming (SQP), and interior-point. The fmincon function is subjected to the adjustment technique to search for the minimum number of actuators and optimum actuation. The algorithm excludes inactive actuators in several iterations. In this research, the 21st iteration gave optimum results, using 802 actuators and a total actuation of 1493 mm. MATLAB analyzes the structure before and after adjustment and finds the optimum actuator set. In addition, the optimal actuation found in MATLAB is applied to the modeled structure in MATLAB and SAP2000 to verify MATLAB results.

关键词： optimization algorithms spherical structures double layers shape and stress control actuator

Multi-constraint optimization for Energy Scheduling of Plug-In Electric Vehicles 8th

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Multi-constraint Optimization for Energy Scheduling of Plu...

8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Linxin Yang, Zhile Guo, Yuanjun Xiao, Qinge Zhu, Juncheng Faculty of Data Science City University of Macau Taipa999078 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China

ISBN: (纸本)9789819602315

The integration of a substantial number of plug-in electric vehicles (PEVs) into power grid scheduling introduces complexities due to stochastic charging and discharging behaviors, which pose significant challenges to the stability and reliability of the power system. The PEV scheduling optimization extends the traditional combinatorial optimization problem, adding constraints and variables, and becomes a multi-constraint unit commitment (MCUC)problem. In this paper, a novel PEV scheduling optimization framework is proposed to address MCUC problem, taking economic cost and carbon emissions as the objectives, a multi-constraint mathematical model is established. Meanwhile, a new binary level-based learning optimization algorithm is proposed to solve flexible scheduling of PEVs in power systems. Finally, considering different numbers of units, the superiority of the proposed optimization algorithm is verified by comparison with different algorithms. The feasibility and effectiveness of the proposed solution are also analyzed before and after scheduling to verify its potential in solving the MCUC problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

An Adaptive Dropout Approach for High-Dimensional Bayesian optimization

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arXiv 2025年

作者： Huang, Jundi Zhan, Dawei School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensionality of the acquisition function. In the proposed algorithm, we adaptively dropout the variables of the acquisition function along the iterations. By gradually reducing the dimension of the acquisition function, the proposed approach has less and less difficulty to optimize the acquisition function. Numerical experiments demonstrate that AdaDropout effectively tackle high-dimensional challenges and improve solution quality where standard Bayesian optimization methods often struggle. Moreover, it achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches. This work provides a simple yet efficient solution for high-dimensional expensive optimization. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Performance-Driven optimization of Parallel Breadth-First Search

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arXiv 2025年

作者： Bhaskar, Marati Kanakagiri, Raghavendra Department of Computer Science and Engineering IIT Tirupati India

Breadth-first search (BFS) is a fundamental graph algorithm that presents significant challenges for parallel implementation due to irregular memory access patterns, load imbalance and synchronization overhead. In this paper, we introduce a set of optimization strategies for parallel BFS on multicore systems, including hybrid traversal, bitmap-based visited set, and a novel non-atomic distance update mechanism. We evaluate these optimizations across two different architectures - a 24-core Intel Xeon platform and a 128-core AMD EPYC system - using a diverse set of synthetic and real-world graphs. Our results demonstrate that the effectiveness of optimizations varies significantly based on graph characteristics and hardware architecture. For small-diameter graphs, our hybrid BFS implementation achieves speedups of 3 − 8× on the Intel platform and 3 − 10× on the AMD system compared to a conventional parallel BFS implementation. However, the performance of large-diameter graphs is more nuanced, with some of the optimizations showing varied performance across platforms including performance degradation in some cases. © 2025, CC BY.

关键词： optimization algorithms

An Ensemble Niching Technique-Based Differential Evolution for Multimodal optimization Problems 19th

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An Ensemble Niching Technique-Based Differential Evolution f...

19th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Liang, Shao-Min Wang, Zi-Jia Chen, Zong-Gan Zhan, Zhi-Hui School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China School of Computer Science South China Normal University Guangzhou510898 China School of Computer Science and Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9789819623754

The multimodal optimization problems (MMOPs) require finding multiple optima simultaneously. Evolutionary computation integrated with niching techniques is commonly used to solve MMOPs. However, different niching techniques are suitable for different situations. In order to make the niche techniques perform better in different problems, in this paper, we propose an ensemble niching technique (ENT). In ENT, three different niching techniques, crowding clustering, speciation clustering, and nearest-better clustering (NBC), are used to identify the potential niche centers. Based on the potential niche center, the probability of an individual being a true niche center is calculated and the true niche center is identified. The identified true niche center can form niches more properly which combines the superiority of different niche techniques. Based on ENT, we combine it with DE and propose a novel multimodal optimization algorithm ENTDE. Meanwhile, we propose a dual-strategy update mechanism (DUM) to select the suitable mutation strategy in different evolutionary states for better evolution. To testify to the effectiveness of the proposed algorithm, ENTDE and eleven state-of-the-art algorithms are experimented on the widely used CEC2013 multimodal benchmark test suite. The experimental results indicate that ENTDE is significantly better than the compared algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

TYPICALITY OF PERIODIC optimization OVER AN EXPANDING CIRCLE MAP

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arXiv 2025年

作者： Gao, Rui Shen, Weixiao Zhang, Ruiqin School of Mathematical Sciences Qufu Normal University Jining273165 China Shanghai Center for Mathematical Sciences New Cornerstone Science Laboratory Fudan University Jiangwan Campus No 2005 Songhu Road Shanghai200438 China School of Mathematical Sciences Fudan University No 220 Handan Road Shanghai200433 China

We study the ergodic optimization problem over a real analytic expanding circle map. We show that in both the topological and the measure-theoretical senses, a typical Cr performance function has a unique maximizing measure and the unique maximizing measure is supported on a periodic orbit, for r = 1, 2, · · ·, ∞, ω. © 2025, CC BY.

关键词： optimization algorithms

An Improved Method for Parameter Identification of J-A Models

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An Improved Method for Parameter Identification of J-A Model...

7th International Conference on Electronics Technology, ICET 2024

作者： Zhu, Xulai Shen, Shuai Zhang, Gongquan Wang, Zongyao Anhui Jianzhu University Anhui Provcince Key Laboratory of Intelligent Building and Building Energy Saving Hefei China

ISBN: (纸本)9798350363951

The traditional J-A hysteresis model parameter identification method generally suffers from the problems of time-consuming and low accuracy. This paper proposed an improved dung beetle optimization algorithm (IDBO) to realize accurate, effective and fast identification of J-A hysteresis model parameters. The algorithm uses the different behaviors of dung beetles as operators of the algorithm. The algorithm's selection strategy utilizes chaotic mapping theory to initialize the adaptive weights of the population and rolling beetles, resulting in algorithms that balances global and local exploration. The improved dung beetle optimization algorithm is used to identify the parameters of the J-A hysteresis model and generate hysteresis curves, which are compared with the classical particle swarm optimization (PSO) algorithm and the unimproved dung beetle optimization (DBO) algorithm. Comparison results show that the improved dung beetle optimization algorithm improves the solution speed and accuracy by a factor of 20 compared to PSO and DBO, and the adaptation value of the convergence can be up to 5.5 × 10-4 that is two orders of magnitude higher. IDBO is able to accurately identify hysteresis curve and has a small tolerance with the survey curve, which proves the effectiveness and practicality of the algorithm. © 2024 IEEE.

关键词： optimization algorithms

A comprehensive review of microgrid control methods: Focus on AI, optimization, and predictive techniques

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 125卷

作者： Limouni, Tariq Yaagoubi, Reda Bouziane, Khalid Guissi, Khalid Baali, El Houssain Hassan II Inst Agron & Vet Energy & Agroequipment Dept Rabat 10112 Morocco Hassan II Inst Agr & Vet Med Sch Geomatics & Surveying Engn Rabat 10112 Morocco Int Univ Rabat Campus UIR Higher Sch Energy Engn LERMA Sale 11100 Morocco

With the depletion of fossil fuels, the integration of renewable energy sources as distributed energy resources has become mandatory. However, the uncertainty and intermittent nature of these sources introduce significant challenges to their integration into microgrids. Effective control systems are essential for ensuring smooth integration, managing energy storage systems, and maintaining microgrid safety. In this study, a review of recent control methods applied in microgrid management was conducted with a focus on AI, optimization, and predictive techniques. These advanced and intelligent control methods were chosen for their potential to address current challenges. This study examined the benefits, limitations, and areas for future improvement. In addition, it explores the potential and the challenges of hybrid control techniques, which are less discussed in the literature, to further enhance control system efficiency and performance.

关键词： Artificial intelligence optimization algorithms Metaheuristics algorithms Model predictive control Microgrid control Hybrid control technique Power and energy management Fault detection