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arXiv 2025年

作者： Kwon, Deuksin Hae, Jiwon Clift, Emma Shamsoddini, Daniel Gratch, Jonathan Lucas, Gale M. University of Southern California United States St. Olaf College United States Northwestern University United States

Negotiation requires dynamically balancing self-interest and cooperation to maximize one’s own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. To address this, we introduce principle-driven negotiation agents, powered by ASTRA, a novel framework for turn-level offer optimization grounded in two core principles: opponent modeling and Tit-for-Tat reciprocity. ASTRA operates in three stages: (1) interpreting counterpart behavior, (2) optimizing counteroffers via a linear programming (LP) solver, and (3) selecting offers based on negotiation tactics and the partner’s acceptance probability. Through simulations and human evaluations, our agent effectively adapts to an opponent’s shifting stance and achieves favorable outcomes through enhanced adaptability and strategic reasoning. Beyond improving negotiation performance, it also serves as a powerful coaching tool, offering interpretable strategic feedback and optimal offer recommendations. © 2025, CC BY.

关键词： linear programming

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A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise Signal processing algorithms Clustering algorithms linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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Distributed Positioning Method for Non-Cooperative Underwater Targets Based on Multipath Time Delay Differences

Distributed Positioning Method for Non-Cooperative Underwate...

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Sensors and Information Technology (ICSI), International Conference on

作者： Linchuan Sun Yuchen Hong Zhuoran Liu Guoqing Jiang Science and Technology on Sonar Laboratory Hangzhou Applied Acoustics Research Institute Hangzhou China Hanjiang National Laboratory Wuhan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Distributed underwater acoustic positioning systems offer the advantages of high positioning accuracy and flexible coverage. Classical non-cooperative target positioning methods in underwater acoustics require the aggregation of time-domain data from various nodes, leading to high network pressure, and positioning accuracy is degraded by multipath interference. To address this issue, this paper presents a distributed positioning method for non-cooperative underwater targets based on Multipath Time Delay Differences (MPD). This method leverages a functional mapping relationship between multipath time delay differences, positions of measurement units, and the target position, utilizing the principle of multipath propagation. An objective function is constructed using the least mean square criterion, and the target position is obtained through an optimization algorithm. Simulation analysis results demonstrate that, compared to traditional positioning methods based on Time Difference of Arrival (TDOA), Time of Arrival (TOA), and Direction of Arrival (DOA), the proposed method based on multipath time delay differences exhibits significant improvements in positioning accuracy, particularly in depth accuracy. Furthermore, this method eliminates the need to aggregate time-domain signals from each measurement unit, significantly reduced network data transmission pressure and enhanced the stability and reliability of the positioning system.

关键词： Accuracy Measurement units Delay effects Stability criteria Interference linear programming Reliability Underwater acoustics Time-domain analysis Optimization

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A Surrogate-Assisted Continuous Ant Colony Optimization Method for Stochastic Optimization

A Surrogate-Assisted Continuous Ant Colony Optimization Meth...

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Multidisciplinary Computational Intelligence Incubators (MCII Companion), IEEE Symposium for

作者： Hao Cong Feng-Feng Wei Wei-Neng Chen School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798331519667

ISBN: (纸本)9798331519674

Stochastic optimization problems, which involve random variables in the optimization process, are commonly seen in many applications such as engineering design and logistics management. The challenge of stochastic optimization is how to accurately evaluate the value of the objective function, while using a large number of Monte Carlo simulations for evaluation can result in significant computational costs. In this paper, we propose a surrogate-assisted continuous ant colony optimization method for stochastic optimization (SAACO). First, instead of using a lot of Monte Carlo simulations, SSACO estimates the approximated fitness values of the individuals in the population by a surrogate model. Second, the continuous ant colony optimization is used as the optimizer, where the population is evolved by constructing a Gaussian kernel function from global information to construct the promising solutions. The surrogate model is a forward model from the solution space to the objective space, while the Gaussian kernel function can be regarded as an inverse model from the objective space to the solution space. By combining two models, the performance of solving stochastic optimization problems can be improved. Experimental results demonstrate the promising performance of the proposed SAACO.

关键词： Ant colony optimization Monte Carlo methods Computational modeling Stochastic processes linear programming Computational efficiency Random variables Kernel Optimization Logistics

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Weighted group search on the disk & improved lower bounds for priority evacuation

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Journal of Computer and System Sciences 2025年 154卷

作者： Georgiou, Konstantinos Wang, Xin Department of Mathematics Toronto Metropolitan University TorontoON Canada

We study weighted group search on a disk, where two unit-speed agents must locate a hidden target exactly distance 1 away (within a unit-radius disk), starting from the same point. Agents share findings instantly via the wireless model. The goal is to minimize the worst-case weighted average of their arrival times, with one agent weighted 1 and the other w∈[0,1]. This problem extends prior work on search on a line (with known optimal strategies) and the priority evacuation problem, which corresponds to w=0 and still has a notable gap between upper and lower bounds. Our contributions are the following: (1) Upper bounds for all w, using refined known techniques. (2) A novel lower-bound framework using linear programming, inspired by metric embeddings, and (3) Improved bounds for priority evacuation, raising the lower bound from 4.38962 to 4.56798. © 2025 The Author(s)

关键词： linear programming

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A Neurodynamic Approach for Distributed Optimization with Delayed Feedback

A Neurodynamic Approach for Distributed Optimization with De...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Yucheng Qi Mengxin Wang Xinrui Jiang Sitian Qin Department of Mathematics Harbin Institute of Technology (Weihai) Weihai PR China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

In this paper, a neurodynamic approach with communication delay is proposed for the sake of solving distributed optimization problems. First, the relationship between the equilibrium of the algorithm and the optimal solution of the distributed optimization problem is established. Then, sufficient conditions for the equilibrium to converge to the optimal solution of the optimization problem are provided in the form of linear matrix inequalities, in the case of communication delay. In addition, numerical simulation verifies the validity of the neurodynamic approach with communication time delay.

关键词： Neurodynamics Sufficient conditions linear programming Numerical simulation linear matrix inequalities Delays Topology Optimization Multi-agent systems Intelligent control

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Optimization of Sliding Mode Parameters Using Chess Algorithm to Control the Position of a linear DC Motor

Optimization of Sliding Mode Parameters Using Chess Algorith...

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International Electrical Engineering Congress (iEECON)

作者： Supakan Matawong Sitthisak Audomsi Saravuth Pothiya Adithep Chaisawasd Ekkawit Thaokaew Nukanda Thanyacharoen Pakasit Malin Worawat Sa-Ngiamvibool Supannika Wattana Faculty of Engineering Mahasarakham University Thailand Production Solutions (Thailand) Co. Ltd. Rayong Thailand Division of Building and Ground Mahasarakham University Thailand Faculty of Medicine Mahasarakham University Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

DC motors find extensive application in various industries because of their accuracy in controlling torque and speed, which makes them crucial for machine operation, transportation and automation systems. Sliding mode control (SMC) offers enhanced resilience to system uncertainties and disturbances, despite the widespread use of conventional PID controllers. This study looks at how optimization algorithms, specifically the Chess Optimization Algorithm (COA) and Particle Swarm Optimization (PSO), can be used to fine-tune SMC parameters in order to make a linear DC motor work better. The study uses Integral Absolute Error (IAE) and Integral Square Error (ISE) as objective functions to look at things like rise time, overshoot, settling time and steady-state error that happen over time. The results show that COA is better than PSO because it has a shorter rise time (1.1245s for IAE and 1.4843s for ISE), less overshoot (7.0404x10 -5 % for IAE and 8.7887x10 -5 % for ISE) and less errors, even though PSO has slightly shorter settling times for ISE. The Chess Optimization Algorithm exhibits remarkable stability and accuracy, positioning it as a highly effective approach for optimizing intricate control systems such as SMC in linear DC motors.

关键词： Accuracy Uncertainty Transportation DC motors linear programming Steady-state Particle swarm optimization Optimization Sliding mode control Tuning

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Optimal Planning Method of AC/DC Microgrid Based on Node Coupling Degree and Power Balance Degree

Optimal Planning Method of AC/DC Microgrid Based on Node Cou...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Minghong Liu Mengke Liao Xin Yuan Zhaoqun Zhu Qing Li State Grid Xinjiang Economic Research Institute Urumqi Xinjiang China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

In the planning of an Alternating Current/Direct Current (AC/DC) microgrid, there are too many interference factors, which leads to the difference between the planning results and the actual situation. Therefore, an optimal planning method for AC/DC microgrid based on node coupling degree and power balance degree is proposed. Under the analysis of node coupling degree and power balance degree, the optimal planning structure of the AC/DC microgrid is constructed to achieve maximum power output. Based on the objective function of the upper planning structure and the objective function of the lower planning structure, the objective function of AC/DC microgrid optimization planning is calculated, the data of AC/DC microgrid is dimensionless, the standardization rules are formulated, and the optimal planning method flow of AC/DC microgrid is designed to realize the optimal planning design. The experimental results show that the utilization rate of this method is above 90%, and the application effect is good, which can meet the power target of AC/DC microgrid optimization planning.

关键词： Couplings Microgrids Standardization Interference linear programming Planning Smart grids Reliability Artificial intelligence Optimization

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A Novel Optimized Li-ion Battery Charging Protocol for EV Onboard Chargers

A Novel Optimized Li-ion Battery Charging Protocol for EV On...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Mohammad Reza Gholipour Mehdi Saradarzadeh Ehsan Asadi Shahrokh Farhangi School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Faculty of Electrical and Computer Engineering Jundi-Shapur University of Technology Dezful Iran

ISBN: (数字)9798331533946

ISBN: (纸本)9798331533953

An optimal charging profile for Li-ion batteries is proposed in this paper. The objective of the charging process is to minimize the charging time of a Li-ion battery while concurrently minimizing its energy losses. To address this challenge, a multi-objective evolutionary optimization algorithm, SPEA2, is employed. For this purpose, the extraction of electrical parameters for the equivalent circuit model of the Li-ion battery is addressed. Thereafter, the polarization voltages of the Li-ion battery are utilized as a key criterion in the SPEA2 optimization algorithm. Additionally, a power loss analysis of the onboard charger converter is conducted, and simulation results are presented to validate the theoretical analysis. The results demonstrate that the proposed charging method has reduced the charging time by $\mathbf{1 6. 1 7 \%}$ and the power losses of the charger system by 6.08% compared to the Constant Current-Constant Voltage (CC-CV) charging profile.

关键词： Lithium-ion batteries Energy loss Protocols Simulation linear programming Power electronics Integrated circuit modeling Optimization Standards Equivalent circuits

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A Multi-Optimizer Metaheuristic Approach for Solving Constrained Patch Scheduling Problem

A Multi-Optimizer Metaheuristic Approach for Solving Constra...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Louay Karadsheh Faten Hamad Hussam Fakhouri Faculty of Information Technology University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman The University of Jordan Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall performance. In this work, we optimize the problem of constrained patch scheduling using a multi-optimizer metaheuristic approach. We first model patch scheduling as an optimization problem aiming to minimize both total downtime and vulnerability exposure while also respecting concurrency limits, which restrict the number of machines simultaneously offline. The objective function incorporates a penalty factor to discourage over-concurrent patching. We compare eight modern metaheuristic algorithms with variable patch durations. Extensive experiments are conducted, analyzing best objective scores, downtime, exposure, concurrency penalty, computational time, and convergence trends. **Our findings indicate that Equilibrium Optimizer (EO) achieves the lowest final cost (6.5351), followed closely by Multi-Verse Optimizer (MVO) (6.8295). Whereas Grey Wolf Optimizer (GWO) and Moth Flame Optimization (MFO), yield moderate results, while Whale Optimization Algorithm (WOA) exhibits the highest cost (10.3944). These outcomes underscore the impact of concurrency-limited patching constraints on performance and emphasize the benefits of selecting optimizers that effectively balance exploration and exploitation for scheduling in security-critical environments.

关键词： Concurrent computing Costs Processor scheduling Metaheuristics Fires Market research linear programming Whale optimization algorithms Security Convergence

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