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Landscape Features in Single-Objective Continuous optimization: Have We Hit a Wall in Algorithm Selection Generalization?

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arXiv 2025年

作者： Cenikj, Gjorgjina Petelin, Gašper Seiler, Moritz Cenikj, Nikola Eftimov, Tome

The process of identifying the most suitable optimization algorithm for a specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models can generalize effectively to novel, unseen problems. This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization. In particular, it considers the most widely used Exploratory Landscape Analysis features, as well as recently proposed Topological Landscape Analysis features, and features based on deep learning, such as DeepELA, TransOptAS and Doe2Vec. Our results indicate that when presented with out-of-distribution evaluation data, none of the feature-based AS models outperform a simple baseline model, i.e., a Single Best Solver. © 2025, CC BY-NC-ND.

关键词： optimization algorithms

Exploiting Partial Assignments in optimization Modulo Theories

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arXiv 2025年

作者： Masina, Gabriele Sebastiani, Roberto DISI University of Trento Trento Italy

optimization Modulo Theories (OMT) extends Satisfiability Modulo Theories (SMT) with the task of optimizing some objective function(s). In OMT solvers, a CDCL-based SMT solver enumerates theory-satisfiable total truth assignments, and a theory-specific procedure finds an optimum model for each of them;the current optimum is then used to tighten the search space for the next assignments, until no better solution is found. In this paper, we analyze the role of truth-assignment enumeration in OMT. First, we spotlight that the enumeration of total truth assignments is suboptimal, since they may over-restrict the search space for the optimization procedure, whereas using partial truth assignments instead can improve the effectiveness of the optimization. Second, we propose some reduction techniques for better exploiting partial assignments in the OMT context. We implemented these techniques in the OPTIMATHSAT solver, and conducted an experimental evaluation on OMT(LRA) benchmarks. The results support the efficiency and effectiveness of our approach. © 2025, CC BY-SA.

关键词： optimization algorithms

Higher-Order Stochastic Dominance Constraints in optimization

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arXiv 2025年

作者： Lakshmanan, Rajmadan Pichler, Alois Kopa, Miloš Faculty of Mathematics University of Technology Chemnitz Germany Charles University Prague Czech Republic

This contribution examines optimization problems that involve stochastic dominance constraints. These problems have uncountably many constraints. We develop methods to solve the optimization problem by reducing the constraints to a finite set of test points needed to verify stochastic dominance. This improves both theoretical understanding and computational efficiency. Our approach introduces two formulations of stochastic dominance—one employs expectation operators and another based on risk measures—allowing for efficient verification processes. Additionally, we develop an optimization framework incorporating these stochastic dominance constraints. Numerical results validate the robustness of our method, showcasing its effectiveness for solving higher-order stochastic dominance problems, with applications to fields such as portfolio *** Codes 90C15, 91B06, 60E15 © 2025, CC BY.

关键词： optimization algorithms

The Distributionally Robust optimization Model of Sparse Principal Component Analysis

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arXiv 2025年

作者： Wang, Lei Liu, Xin Chen, Xiaojun Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

We consider sparse principal component analysis (PCA) under a stochastic setting where the underlying probability distribution of the random parameter is uncertain. This problem is formulated as a distributionally robust optimization (DRO) model based on a constructive approach to capturing uncertainty in the covariance matrix, which constitutes a nonsmooth constrained min-max optimization problem. We further prove that the inner maximization problem admits a closed-form solution, reformulating the original DRO model into an equivalent minimization problem on the Stiefel manifold. This transformation leads to a Riemannian optimization problem with intricate nonsmooth terms, a challenging formulation beyond the reach of existing algorithms. To address this issue, we devise an efficient smoothing manifold proximal gradient algorithm. We prove the Riemannian gradient consistency and global convergence of our algorithm to a stationary point of the nonsmooth minimization problem. Moreover, we establish the iteration complexity of our algorithm. Finally, numerical experiments are conducted to validate the effectiveness and scalability of our algorithm, as well as to highlight the necessity and rationality of adopting the DRO model for sparse PCA. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

Sequential Quadratic optimization for Solving Expectation Equality Constrained Stochastic optimization Problems

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arXiv 2025年

作者： Shen, Haoming Zeng, Yang Zhou, Baoyu Department of Industrial Engineering University of Arkansas United States School of Computing and Augmented Intelligence Arizona State University United States

A sequential quadratic programming method is designed for solving general smooth nonlinear stochastic optimization problems subject to expectation equality constraints. We consider the setting where the objective and constraint function values, as well as their derivatives, are not directly available. The algorithm applies an adaptive step size policy and only relies on objective gradient estimates, constraint function estimates, and constraint derivative estimates to update iterates. Both asymptotic and non-asymptotic convergence properties of the algorithm are analyzed. Under reasonable assumptions, the algorithm generates a sequence of iterates whose first-order stationary measure diminishes in expectation. In addition, we identify the iteration and sample complexity for obtaining a first-order Ε-stationary iterate in expectation. The results of numerical experiments demonstrate the efficiency and efficacy of our proposed algorithm compared to a penalty method and an augmented Lagrangian method. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

IMPROVED FOX optimization ALGORITHM A PREPRINT

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arXiv 2025年

作者： Jumaah, Mahmood A. Ali, Yossra H. Rashid, Tarik A. Department of Computer Science University of Technology Baghdad10066 Iraq Department of Computer Science and Engineering AIIC University of Kurdistan Hewlêr Erbil44001 Iraq

optimization algorithms are essential for solving many real-world problems. However, challenges such as premature convergence to local optima and the difficulty of effectively balancing exploration and exploitation often hinder their performance. To address these issues, this paper proposes an improved FOX optimization algorithm, Improved FOX (IFOX). The IFOX algorithm introduces a new adaptive mechanism for balancing exploration and exploitation based on fitness values. It also reduces the number of hyperparameters and simplifies the core equations of the original FOX. To evaluate its effectiveness, IFOX has been tested on classical uni-modal and multi-modal benchmark functions, as well as on benchmark sets from the Congress on Evolutionary Computation (CEC), in addition to two engineering design problems: Pressure Vessel Design and Economic Load Dispatch. The results show that IFOX outperforms existing optimization algorithms, achieving superior results on 51 benchmark functions. These findings underscore the strong potential of IFOX as a competitive and robust optimization algorithm for a wide range of applications. © 2025, CC BY.

关键词： optimization algorithms

The use of trust region algorithms to exploit discrepancies in function computation time within optimization models

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JOURNAL OF MECHANICAL DESIGN 1999年第4期121卷 552-556页

作者： Nelson, SA Papalambros, PY Univ Michigan Dept Mech Engn & Appl Mech Ann Arbor MI 48109 USA

Computational models in engineering often use a range of analysis functions within the same optimization problem, from finite element models to analytical expressions. The computational expense of these models often differs by orders of magnitude, and practical optimization algorithms should address this discrepancy. In this article, in an effort to reduce the number of expensive analyses, a technique is discussed for modifying trust region algorithms to utilize the inexpensive functions directly when calculating iterates. The technique is implemented with a trust region algorithm of Yuan and applied to valve event optimization of an internal combustion engine.

关键词： Computational modeling Trust trust algorithms computational time Region gasoline engine optimization algorithms Nonprofit Organizations Function

optimization of design parameters and 3D-printing orientation to enhance the efficiency of topology-optimized components in additive manufacturing

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Results in Materials

Results in Materials 2025年 26卷

作者： Efa, Dame Alemayehu Ifa, Dejene Alemayehu School of Mechanical Engineering Institute of Technology Wallaga University P.O. Box 395 Nekemte Ethiopia

Computer-aided design (CAD) is revolutionizing 3D object production in Additive Manufacturing (AM), especially enhancing the creation of complex optimized structures. However, due to the difficulties of testing the thickness or size of structural components using physical testing in topology-optimized parts, optimizing parts for high efficiency remains a significant challenge. In addition to optimizing 3D printing orientation, this study uses Response Surface Methodology (RSM) and optimization algorithms to optimize design parameters such as safety factor and thickness for predicting stress and deformation in topology optimization. The results show that RSM, Artificial Neural Network (ANN) and observed simulation results strongly correlate, confirming their reliability for determining the most optimal design parameters for topology optimization. Maximum displacement and Von-Mises stress at 0.101 mm and 29.1 MPa were found to be the optimal responses. In contrast, the optimum input parameters include a minimum safety factor of 1.2, a minimum thickness of 3.85 mm, and a maximum thickness of 8.42 mm, which are identified for optimal topology optimization using Genetic algorithms (GA). These results were verified using optimization software, confirming the effectiveness of the study's methodology. Vertical orientation is the optimal printing orientation to develop parts with greater hardness, according to X-ray Diffraction (XRD), hardness, wear resistance, and morphological tests. Testing confirmed that the topology-optimized result, which utilized the most optimal process condition and orientation, effectively produced a stronger 3D-printed part. Therefore, this method saves material and the time waste usually caused by trial and error by predicting the optimal 3D printing orientation and design parameters in providing more effective and intended results. © 2025 The Authors

关键词： Additive manufacturing optimization algorithms Parametric optimization Printing orientation Topology optimization

Enhancing power system stability by coordinating a wind turbine voltage regulator and lead-lag power system stabilizer using GOOSE optimization

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Scientific reports 2025年第1期15卷 15242-15242页

作者： Ibrahim, Nader M A El-Fergany, Attia A. Hemade, Bassam A. Department of Electrical Faculty of Technology and Education Suez University P.O. Box: 43221 Suez Egypt Electrical Power and Machines Department Zagazig University Zagazig 44519 Egypt Department of Electrical Faculty of Technology and Education Suez University P.O. Box: 43221 Suez Egypt

Integrating wind energy into power systems can negatively impact stability by reducing oscillation damping. Wind Turbine Voltage Regulators (WT VRs) are designed to manage reactive power and maintain voltage stability;however, they often do not coordinate effectively with Power System Stabilizers (PSS) from synchronous generators (SG). This study utilizes the GOOSE optimization Algorithm (GOA) to optimize and coordinate the gains of the WT proportional-integral virtual regulator (WT PI-VR) and the SG proportional-integral-type lead-lag PSS (PI-type LL-PSS), to enhance power system stability and performance. The GOA performance compared with the Osprey optimization Algorithm (OOA) and Particle Swarm Optimizer (PSO). The PI-type LL-PSS performance is compared with proportional-integral-derivative PID-PSS configurations, highlighting its robustness. Testing scenarios include step changes, voltage sags, and three-phase short-circuit faults, using metrics like integral time absolute error, settling time, and standard deviation for robustness evaluation. Statistical analysis shows several benefits from the proposed methodology: (i) A 48.85% stability improvement in coordinating WT PI-VR with PID-PSS using GOA versus OOA, (ii) A 24.40% performance boost with GOA over OOA using PI-type LL-PSS, (iii) A 14.4% enhancement when coordinating WT PI-VR with PI-type LL-PSS compared to PID-PSS, and (iv) A 34.23% performance increase using GOA instead of PSO for coordinating WT PI-VR with PI-type LL-PSS. © 2025. The Author(s).

关键词： Controller coordination Doubly fed induction generators optimization algorithms Small-signal stability Wind turbine integration