检索结果-内蒙古大学图书馆

Numerical Investigation of the Swarm Intelligence Algorithm Obtained Using ChatGPT for Univariate Global optimization 23rd

学校读者我要写书评

暂无评论

Numerical Investigation of the Swarm Intelligence Algorithm ...

23rd International Conference on Mathematical optimization Theory and Operations Research, MOTOR 2024

作者： Sorokovikov, Pavel Gornov, Alexander Zarodnyuk, Tatiana Matrosov Institute for System Dynamics and Control Theory of SB RAS Lermontov Str. 134 Irkutsk664033 Russia

ISBN: (纸本)9783031733642

This paper explores the possibility of designing an efficient global optimization algorithm using an artificial intelligence chatbot, ChatGPT. The main idea is to use the swarm intelligence metaheuristic method, which operates on a set of particles in a swarm, to solve non-local univariate search problems. Testing was carried out on a set of one-dimensional non-convex functions with varying numbers of local optima. The results demonstrate that this algorithm can be used to solve multi-extremal optimization problems. The paper presents the results of a numerical study on the properties of this algorithm in comparison with the Piyavsky method, the Strongin method, and a combination of the Strongin with the Parabolas methods. The performed computational experiments testify to the suitability of the obtained swarm intelligence algorithm for solving univariate non-local search problems. This approach can also be useful for solving auxiliary optimization problems and for developing multimethod computational technologies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： optimization algorithms

Revisiting implicit variables in mathematical optimization: simplified modeling and a numerical evidence

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Mehlitz, Patrick Philipps-Universität Marburg Department of Mathematics and Computer Science Marburg35032 Germany

Implicit variables of an optimization problem are used to model variationally challenging feasibility conditions in a tractable way while not entering the objective function. Hence, it is a standard approach to treat implicit variables as explicit ones. Recently, it has been shown in terms of a comparatively complex model problem that this approach, generally, is theoretically disadvantageous as the surrogate problem typically suffers from the presence of artificial stationary points and the need for stronger constraint qualifications. The purpose of the present paper is twofold. First, it introduces a much simpler and easier accessible model problem which can be used to recapitulate and even broaden the aforementioned findings. Indeed, we will extend the analysis to two more classes of stationary points and the associated constraint qualifications. These theoretical results are accompanied by illustrative examples from cardinality-constrained, vanishing-constrained, and bilevel optimization. Second, the present paper illustrates, in terms of cardinality-constrained portfolio optimization problems, that treating implicit variables as explicit ones may also be disadvantageous from a numerical point of *** Codes 49J53, 90C30, 90C33 © 2025, CC BY-NC-SA.

关键词： optimization algorithms

An Optimistic Algorithm for Online Convex optimization with Adversarial Constraints

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lekeufack, Jordan Jordan, Michael I. Department of Statistics University of California Berkeley United States Department of Statistics Department of Electrical Engineering and Computer Science University of California Berkeley United States

We study Online Convex optimization (OCO) with adversarial constraints, where an online algorithm must make repeated decisions to minimize both convex loss functions and cumulative constraint violations. We focus on a setting where the algorithm has access to predictions of the loss and constraint functions. Our results show that we can improve the current best bounds of O(√T) regret and O͂(√T) cumulative constraint violations to O(√ET (f)) and O͂(√ET (g)), respectively, where ET (f) and ET (g) represent the cumulative prediction errors of the loss and constraint functions. In the worst case, where ET (f) = O(T) and ET (g) = O(T) (assuming bounded loss and constraint functions), our rates match the prior O(√T) results. However, when the loss and constraint predictions are accurate, our approach yields significantly smaller regret and cumulative constraint violations. Notably, if the constraint function remains constant over time, we achieve O͂(1) cumulative constraint violation, aligning with prior results. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

Social Distancing Induced Coronavirus optimization Algorithm (COVO): Application to Multimodal Function optimization and Noise Removal

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Varma, Om Ramakisan Kalra, Mala CSE Department NITTTR Chandigarh India

The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to suffer several deaths. Vaccination, masks, and social distancing are the major steps taken to minimize the spread of the deadly COVID-19 virus. Considering the social distance to combat the coronavirus epidemic, a novel bio-inspired metaheuristic optimization model is proposed in this work, and it is termed as Social Distancing Induced Coronavirus optimization Algorithm (COVO). The pace of propagation of the coronavirus can indeed be slowed by maintaining social distance. Thirteen benchmark functions are used to evaluate the COVO performance for discrete, continuous, and complex problems, and the COVO model performance is compared with other well-known optimization algorithms. The main motive of COVO optimization is to obtain a global solution to various applications by solving complex problems with faster convergence. The error in the fitness function for the COVO model is 1.23×10-18, which is significantly lower than the error values achieved by other existing models: EHO at 4.38×10-8, SSA at 10.12, SSO at 4.05×10-10, SFO at 26.5, BOA at 4.20×10-9, BWO at 3.58×10-5, SMO at 1.04, CVOA 2.95×10-8, SRO 2.40×10-8, and GBRUN 13.27. At last, the validated results depict that the proposed COVO optimization has a reasonable and acceptable performance. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

optimization on a Finer Scale: Bounded Local Subgradient Variation Perspective

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Diakonikolas, Jelena Guzmán, Cristóbal Department of Computer Sciences University of Wisconsin-Madison United States Institute for Mathematical and Computational Engineering Faculty of Mathematics School of Engineering Pontificia Universidad Católica de Chile Chile

We initiate the study of nonsmooth optimization problems under bounded local subgradient variation, which postulates bounded difference between (sub)gradients in small local regions around points, in either average or maximum sense. The resulting class of objective functions encapsulates the classes of objective functions traditionally studied in the optimization literature, which are defined based on either Lipschitz continuity of the objective or Hölder/Lipschitz continuity of the function’s gradient. Further, the defined class is richer in the sense that it contains functions that are neither Lipschitz continuous nor have a Hölder continuous gradient. Finally, when restricted to the aforementioned traditional classes of optimization problems, the constants defining the studied classes lead to more fine-grained oracle complexity bounds. Some highlights of our results are that: (i) it is possible to obtain complexity results for both convex and nonconvex optimization problems with (local or global) Lipschitz constant being replaced by a constant of local subgradient variation, corresponding to small local regions and (ii) complexity of the subgradient set around the set of optima – measured by its mean width in a local region around optima – plays a role in the complexity of nonsmooth optimization, particularly in parallel optimization settings. A consequence of (ii) is that for any error parameter ϵ > 0, parallel oracle complexity of nonsmooth Lipschitz convex optimization is lower than its sequential oracle complexity by a factor Ω˜(1ϵ) whenever the objective function is piecewise-linear with the number of pieces polynomial in the dimension and 1/ϵ. This is particularly surprising considering that existing parallel complexity lower bounds are based on such classes of functions. The seeming contradiction is resolved by considering the region in which the algorithm is allowed to query the objective. © 2024, CC BY.

关键词： optimization algorithms

The Whale optimization Algorithm in Abrasive Water Jet Machining of Tool Steel

学校读者我要写书评

暂无评论

Procedia Computer Science 2023年 225卷 1037-1044页

作者： Elżbieta Kawecka The Jacob of Paradies University Faculty of Technology Teatralna 25 66-400 Gorzów Wielkopolski Poland

The paper presents possibility of Whale optimization Algorithm application into abrasive waterjet (AWJ) machining of tool steel. Based on the control parameters of the process of cutting tool steel with AWJ, the objective function was determined using the Response Surface Methodology (RSM). Then the process of optimization and established the set of control parameters which the value of the objective function reaches the extremum value was carried out. Also, the effectiveness of this algorithm for optimizing the parameters of the studied process was determined. The calculated on the basis WOA value of optimum was comparison with value of the best effect determined experimentally.

关键词： optimization algorithms meta-heuristic algorithms Whale optimization Algorithm (WOA) Abrasive Water Jet (AWJ) AWJ cutting process

optimization of thermal conductivity in coir fibre-reinforced PVC composites using advanced computational techniques

学校读者我要写书评

暂无评论

SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Anand, Saksham Gopalan, Venkatachalam Pitchumani, Shenbaga Velu Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamilnadu India Vellore Inst Technol Ctr Adv Mat & Innovat Technol Chennai 600127 Tamilnadu India

This research focuses on enhancing the thermal conductivity of coir fibre-reinforced polyvinyl chloride (PVC) composites using advanced optimization techniques. While coir fibre adds sustainability and biodegradability, it poses challenges in achieving optimal thermal performance when integrated into PVC. To address these challenges, the study uses Response Surface Methodology (RSM) and three nature-inspired optimization methods viz. Particle Swarm optimization (PSO), Dragonfly optimization (DFO) and Cuckoo Search Algorithm (CSA) to improve factors like fibre content, particle size and chemical treatment. A Box-Behnken experimental design helps to create composite samples using hydraulic injection moulding and thermal conductivity is measured with a two-slab guarded hot plate device. Among the optimization methods, CSA emerges as the most effective, achieving a maximum thermal conductivity of 0.801 W/mK with minimal error deviation (0.01-5.5%) by the process parameters such as potassium hydroxide treatment, coir content of 2 wt% and powder diameter of 75 (mu m). DFO delivers consistent results with slightly higher error rates, while PSO demonstrates rapid convergence but greater variability. The comparison shows that CSA performs better, providing a dependable and long-lasting way to create high-quality coir-reinforced PVC composites that are good for industrial use. This work is among the first to compare multiple bio-inspired optimization algorithms for enhancing the thermal properties of coir-reinforced PVC composites, offering a new pathway for developing high-performance, eco-friendly materials for industrial applications.

关键词： Coir fibre PVC composites Thermal conductivity optimization algorithms Response surface methodology (RSM) Particle swarm optimization (PSO) Dragonfly optimization (DFO) Cuckoo search algorithm (CSA) Biodegradable materials Sustainable composites

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

学校读者我要写书评

暂无评论

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

学校读者我要写书评

暂无评论

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