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arXiv 2025年

作者： Fahkar, Ali Polisano, Kévin Gannaz, Irène Achard, Sophie Univ. Grenoble Alpes CNRS Grenoble INP Inria LJK GrenobleF-38000 France Univ. Grenoble Alpes CNRS Grenoble INP G-SCOP Grenoble38000 France

This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

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Multi-objective optimization of thermoelectric cooler using genetic algorithms

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AIP ADVANCES 2019年第9期9卷 095105-095105页

作者： Lu, Tianbo Zhang, Xiang Zhang, Jianxin Ning, Pingfan Li, Yuqiang Niu, Pingjuan Tianjin Polytech Univ Minist Educ Engn Res Ctr High Power Solid State Lighting Appl Tianjin 300387 Peoples R China Tianjin Polytech Univ Sch Mech Engn Tianjin 300387 Peoples R China

The thermoelectric cooler (TEC) is a kind of cooling equipment which used to dissipate heat from the devices by Peltier effect. The cooling capacity (Q(c)) and coefficient of performance (COP) are both significant performance parameters of a thermoelectric cooler. In this article, three-dimensional numerical simulations are carried out by finite element analysis based on the temperature-dependent materials properties. The experimental and geometrical parameters have important effects on the TEC performance which have been analysed, such as electrical current, geometric configuration of thermoelectric leg, Thomson effect, thermal contact resistances and electrical contact resistances. The results show when the Thomson effect is ignored, the maximum difference in the cooling capacity is 7.638 W while the maximum difference in the COP is 0.09. When contact effect is not considered, the maximum difference in the cooling capacity is 22.06 W while the maximum difference in the COP is 0.75. Furthermore, the cooling capacity and COP have also been simultaneously optimized according to the multi-objective genetic algorithm. The best optimal value is obtained making use of TOPSIS (technique for order preference by similarity to an ideal solution) method from Pareto frontier. Investigated on these optimal design parameters which were anticipated to provide real guidance in industry.

关键词： Evolutionary computation Finite-element analysis Ceramics Thermoelectricity Cooling capacity Contact impedance optimization algorithms Electronic devices Thermodynamic states and processes

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Second-order MCSCF optimization revisited. I. Improved algorithms for fast and robust second-order CASSCF convergence

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JOURNAL OF CHEMICAL PHYSICS 2019年第19期150卷 194106-194106页

作者： Kreplin, David A. Knowles, Peter J. Werner, Hans-Joachim Univ Stuttgart Inst Theoret Chem Pfaffenwaldring 55 D-70569 Stuttgart Germany Cardiff Univ Sch Chem Main BldgPk Pl Cardiff CF10 3AT S Glam Wales

A new improved implementation of the second-order multiconfiguration self-consistent field optimization method of Werner and Knowles [ J. Chem. Phys. 82, 5053 (1985)] is presented. It differs from the original method by more stable and efficient algorithms for minimizing the second-order energy approximation in the so-called microiterations. Conventionally, this proceeds by alternating optimizations of the orbitals and configuration (CI) coefficients and is linearly convergent. The most difficult part is the orbital optimization, which requires solving a system of nonlinear equations that are often strongly coupled. We present a much improved algorithm for solving this problem, using an iterative subspace method that includes part of the orbital Hessian explicitly, and discuss different strategies for performing the uncoupled optimization in a most efficient manner. Second, we present a new solver in which the orbital-CI coupling is treated explicitly. This leads to quadratic convergence of the microiterations but requires many additional evaluations of reduced (transition) density matrices. In difficult optimization problems with a strong coupling of the orbitals and CI coefficients, it leads to much improved convergence of both the macroiterations and the microiterations. Third, the orbital-CI coupling is treated approximately using a quasi-Newton approach with Broyden-Fletcher-Goldfarb-Shanno updates of the orbital Hessian. It is demonstrated that this converges almost as well as the explicitly coupled method but avoids the additional effort for computing many transition density matrices. The performance of the three methods is compared for a set of 21 aromatic molecules, an Fe(II)-porphine transition metal complex, as well as for the [Cu2O2(NH3)(6)](2+), FeCl3, Co-2(CO)(6)C2H2, and Al4O2 complexes. In all cases, faster and more stable convergence than with the original implementation is achieved. Published under license by AIP Publishing.

关键词： Density-matrix optimization algorithms optimization problems Isomerization Slater determinant Transition metals

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Modeling of Gaseous Reduction of Iron Oxide Pellets Using Machine Learning algorithms, Explainable Artificial Intelligence, and Hyperparameter optimization Techniques

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STEEL RESEARCH INTERNATIONAL 2024年第8期95卷

作者： Hosseinzadeh, Masih Kasiri, Norollah Rezaei, Mehran Iran Univ Sci & Technol Sch Chem Petr & Gas Engn Comp Aided Proc Engn CAPE Lab Tehran *** Iran Iran Univ Sci & Technol Sch Chem Petr & Gas Engn Catalyst & Nanomat Res Lab CNMRL Tehran *** Iran

In this study, a novel application of machine learning (ML) is introduced to pellet modeling in the intricate non-catalytic gas-solid reaction of direct reduction of iron oxide in the steel industry. Twenty ML models are developed using four algorithms: multilayer perceptron neural networks (MLPNN), radial basis function neural network (RBFNN), support vector regression, and random forest (RF). Hyperparameter optimization is conducted using Bayesian algorithms, random search, and grid search. The optimum model achieves a mean squared error test of 0.0052 with random RF for the larger dataset (872 samples), while smaller datasets (132, 225, and 242 samples) produce optimum models with MLPNN and RBFNN. Hyperparameters vary between the larger datasets and the smaller datasets. The models offer insight into the complex interactions among variables, including time, temperature, gas composition, hematite composition, pellet radius, and initial pellet porosity, influencing the metallization degree. In this study, the significant role of time and temperature is emphasized, as revealed by explainable artificial intelligence using Shapley additive explanation analysis that utilizes the game theory, and the effects of pellet modeling parameters are elucidated through 3D plots, particularly highlighting the impact of changing H2/CO proportion on metallization degree and carbon deposit. Data gathering: five datasets are collected to study different parameters, including temperature, solid and gas compositions, pellet size and initial porosity, and gas flow. Machine learning results: temperature's paramount influence on iron oxide reduction is confirmed through Shapley additive explanation analysis. The effect of different parameters on the solid conversion is investigated using 3D *** (c) 2024 WILEY-VCH GmbH

关键词： artificial intelligences iron oxide reductions ironmaking machine learning neural networks non-catalytic reactions optimization algorithms

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An analysis of SAT constrained MNK-landscapes as benchmark problems for multi-objective evolutionary algorithms

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Swarm and Evolutionary Computation 2025年 95卷

作者： Honjo Ide, Felipe Aguirre, Hernan Miyakawa, Minami Whitley, Darrel Faculty of Engineering Shinshu University Wakasato 4-17-1 Nagano Nagano380-0928 Japan Department of Computer Science Colorado State University 711 Oval Drive Fort CollinsCO80521 United States

Benchmark problems have been fundamental in advancing our understanding of the dynamics and design of multi-objective evolutionary optimization algorithms. Within the binary domain, there is a lack of multi-objective benchmark problems that can help further research on constrained optimization. This paper presents highly configurable benchmark problems for constrained binary multi-objective optimization combining SAT Constraints, constructed from satisfiability clauses, and MNK-Landscapes. The benchmark problems are scalable in the number of equality and inequality constraints, feasibility-hardness, number of objectives, number of variables, and epistasis between variables. This paper studies how SAT Constraints affect the distribution of feasible solutions in objective and decision spaces and illustrates their impact on the performance and dynamics of multi-objective evolutionary algorithms when solving SAT Constrained MNK-Landscapes. © 2025 Elsevier B.V.

关键词： optimization algorithms

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Cluster validity indices for automatic clustering: A comprehensive review

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Heliyon 2025年第2期11卷 e41953页

作者： Ikotun, Abiodun M. Habyarimana, Faustin Ezugwu, Absalom E. School of Mathematics Statistics and Computer Science University of KwaZulu-Natal King Edward Avenue Pietermaritzburg Campus KwaZulu-Natal Pietermaritzburg 3201 South Africa Unit for Data Science and Computing North-West University 11 Hoffman Street North-West Potchefstroom 2520 South Africa

The Cluster Validity Index is an integral part of clustering algorithms. It evaluates inter-cluster separation and intra-cluster cohesion of candidate clusters to determine the quality of potential solutions. Several cluster validity indices have been suggested for both classical clustering algorithms and automatic metaheuristic-based clustering algorithms. Different cluster validity indices exhibit different characteristics based on the mathematical models they employ in determining the values for the various cluster attributes. Metaheuristic-based automatic clustering algorithms use cluster validity index as a fitness function in its optimization procedure to evaluate the candidate cluster solution's quality. A systematic review of the cluster validity indices used as fitness functions in metaheuristic-based automatic clustering algorithms is presented in this study. Identifying, reporting, and analysing various cluster validity indices is important in classifying the best CVIs for optimum performance of a metaheuristic-based automatic clustering algorithm. This review also includes an experimental study on the performance of some common cluster validity indices on some synthetic datasets with varied characteristics as well as real-life datasets using the SOSK-means automatic clustering algorithm. This review aims to assist researchers in identifying and selecting the most suitable cluster validity indices (CVIs) for their specific application areas. © 2025 The Authors

关键词： Automatic clustering Cluster validity index Clustering Metaheuristic algorithms optimization algorithms

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TSENG-TYPE algorithms FOR SOLVING VARIATIONAL INEQUALITIES OVER THE SOLUTION SETS OF SPLIT VARIATIONAL INCLUSION PROBLEMS WITH AN APPLICATION TO A BILEVEL optimization PROBLEM

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical optimization 2024年第1期6卷 41-57页

作者： Taiwo, Adeolu Reich, Simeon Agarwal, Ravi P. Department of Mathematics The Technion – Israel Institute of Technology Haifa3200003 Israel Department of Mathematics Texas A&M University-Kingsville KingsvilleTX78363 United States

We study a new class of variational inclusion problems in the framework of real Hilbert spaces. We propose two Tseng-type algorithms with inertial extrapolation for solving these problems and carry out the convergence analysis of these two methods. We also give an application of our results to solve convex bilevel optimization problems. © 2024 Journal of Applied and Numerial optimization.

关键词： optimization algorithms

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Integration of the reptile search algorithm and the adaptive neuro-fuzzy inference system enhances standardized precipitation evapotranspiration index forecasting

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Scientific Reports 2025年第1期15卷 1-21页

作者： Kayhomayoon, Zahra Bahmani, Mohammad Javad Ghordoyee Milan, Sami Bazrafshan, Ommolbanin Berndtsson, Ronny Department of Geology Payame Noor University Tehran Iran Department of Water Resources Engineering Faculty of Civil Engineering Azad University Tehran Iran Water Engineering Department Faculty of Agricultural Technology University College of Agriculture & Natural Resources University of Tehran Tehran Iran Department of Natural Resources Engineering Faculty of Agricultural Engineering and Natural Resources University of Hormozgan Bandar Abbas Iran Centre for Advanced Middle Eastern Studies & Division of Water Resources Engineering Lund University Lund Sweden

A novel metaheuristic algorithm called the reptile search algorithm (RSA) was introduced in conjunction with artificial neural fuzzy inference system (ANFIS) for the estimation of standardized precipitation evapotranspiration index (SPEI). The model was tested in three different climates: arid and super-cold, semi-arid and cold, and semi-arid and moderate climate across Iran by combining meteorological indices (minimum temperature, maximum temperature, average temperature, precipitation, and potential evapotranspiration) and large-scale climate signals (North Atlantic Oscillation, Arctic Oscillation, Pacific Decadal Oscillation, and Southern Oscillation Index). The results of the ANFIS + RSA model were compared with those of the ANFIS + WOA and ANFIS + GWO models for evaluation. Based on the estimation results and error evaluation criteria, the performance of the ANFIS + RSA model is considered appropriate, showing a higher relative accuracy compared to ANFIS, ANFIS + GWO, and ANFIS + WOA. In semi-arid and moderate climates, the ANFIS + RSA model exhibited the highest prediction accuracy, with RMSE = 0.28, MAE = 0.20, CA = 0.19, and NASH = 0.91. In semi-arid and cold climates, the model’s accuracy was slightly lower, with RMSE = 0.33, MAE = 0.23, CA = 0.23, and NASH = 0.85. In arid and super-cold climates, the model’s accuracy remained relatively consistent, with RMSE = 0.24, MAE = 0.18, CA = 0.19, and NASH = 0.84. Furthermore, the promising results of the hybrid ANFIS + RSA model can be further evaluated in other regions and climates to assess its overall effectiveness. © The Author(s) 2025.

关键词： Drought Extreme events Large-scale climate signals Machine learning optimization algorithms Standardized precipitation evapotranspiration index (SPEI)

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Advancements in optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based algorithms

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algorithms 2024年第9期17卷 416页

作者： Rashed, Noor A. Ali, Yossra H. Rashid, Tarik A. Univ Technol Baghdad Comp Sci Dept Baghdad 10081 Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil 44001 Iraq

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000-2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization.

关键词： single-objective optimization metaheuristic algorithms multi-objective optimization optimization algorithms evolutionary algorithms scheduling algorithmic advancements

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Quantum algorithms for Non-smooth Non-convex optimization

arXiv

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arXiv 2024年

作者： Liu, Chengchang Guan, Chaowen He, Jianhao Lui, John C.S. The Chinese University of Hong Kong Hong Kong University of Cincinnati United States

This paper considers the problem for finding the (δ,ǫ)-Goldstein stationary point of Lipschitz continuous objective, which is a rich function class to cover a great number of important applications. We construct a novel zeroth-order quantum estimator for the gradient of the smoothed surrogate. Based on such estimator, we propose a novel quantum algorithm that achieves a query complexity of Õ(d3/2δ-1ǫ-3) on the stochastic function value oracle, where d is the dimension of the problem. We also enhance the query complexity to Õ(d3/2δ-1ǫ-7/3) by introducing a variance reduction variant. Our findings demonstrate the clear advantages of utilizing quantum techniques for non-convex non-smooth optimization, as they outperform the optimal classical methods on the dependency of ǫ by a factor of ǫ-2/3 © 2024, CC BY.

关键词： optimization algorithms

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