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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) on Collaborate, Innovate and Empower - Propulsion and Power for a Sustainable Future

作者： Chatel, Arnaud Verstraete, Tom von Karman Inst Fluid Dynam Rhode St Genese Belgium

ISBN: (纸本)9780791887110

This paper investigates the potential multimodality of a radial compressor optimization problem by comparing gradientfree and gradient-based methods in terms of results and computational cost. The turbomachinery application consists in a multipoint optimization of the well-known SRV2 radial compressor. The objective of the optimization is to maximize the total-to-total efficiency of the compressor at peak efficiency while maintaining the operating range of the original impeller. The geometry of the compressor is parametrized using 44 design variables and the fluid domain is automatically discretized using a multi-block structured mesh. The evaluation of the aerodynamic performance is performed using a steady-state RANS CFD solver with the Spalart-Allmaras turbulence model. Gradients of the cost functions with respect to the design variables are, when needed, efficiently evaluated with a discrete adjoint solver. Multimodality, i.e. the existence of multiple local optima within the design space, is currently widely debated and plays an important role in the selection of the optimization method. The present study shows that both optimization algorithms converge to the same solution which seems to suggest that the design space is unimodal for the considered optimization problem.

关键词： Design optimization algorithms Turbomachinery Geometry optimization Reynolds-averaged Navier–Stokes equations Steady state Computational fluid dynamics Turbulence Fluids Impellers Compressors

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Accelerated Asynchronous Parallel Stochastic Gradient Descent Algorithm for High-Dimensional and Sparse Data Low-rank Representation 21

Accelerated Asynchronous Parallel Stochastic Gradient Descen...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Hu, Qicong Wu, Hao College of Computer and Information Science Southwest University Chongqing China

ISBN: (纸本)9798350365221

Data characterized by high dimensionality and sparsity are commonly used to describe real-world node interactions. Low-rank representation (LR) can map high-dimensional and sparse (HDS) data to low-dimensional feature spaces and infer node interactions via modeling data latent associations. Unfortunately, existing optimization algorithms for LR models are computationally inefficient and slowly convergent on large-scale datasets. To address this issue, this paper proposes an Accelerated Asynchronous Parallel Stochastic Gradient Descent (A2 PSGD) Algorithm for High-Dimensional and Sparse Data Low-rank Representation with three fold-ideas: a) establishing a lock-free scheduler to simultaneously respond to scheduling requests from multiple threads;b) introducing a greedy algorithm-based load balancing strategy for balancing the computational load among threads;c) incorporating Nesterov's accelerated gradient into the learning scheme to accelerate model convergence. Empirical studies show that A2 PSGD outperforms existing optimization algorithms for HDS data LR in both accuracy and training time. © 2024 IEEE.

关键词： optimization algorithms

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A Multi-objective Binary Differential Evolution Operator for Feature Selection

A Multi-objective Binary Differential Evolution Operator for...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Dehnad, Parastoo Bidgoli, Azam Asilian Rahnamayan, Shahryar Tabriz University Tabriz Iran Wilfrid Laurier University Faculty of Science WaterlooON Canada Lab Department of Engineering St. CatherinesON Canada

ISBN: (纸本)9781665410205

Feature selection is a pivotal component of machine learning and data analysis, to optimize model performance by eliminating irrelevant and redundant features, to address the challenges associated with the 'curse of dimensionality' and interpretability. In this context, we present feature selection as a multi-objective binary optimization task with the dual aim of maximizing classification accuracy while minimizing the number of selected features. In order to address this optimization challenge, we introduce the Multi-objective Binary Differential Evolution algorithm (MOBDE). It's worth noting that DE originally is an extremely powerful real-value coded algorithm, and to make it binary, set-based operators must replace vector-based operations. optimization in binary space is deemed more suitable than real-value optimization because a many-to-one mapping can waste the efforts of the optimizer when solving a binary problem like feature selection. The algorithm leverages a partial opposition-based binary operator to generate diverse solutions to enhance its exploration within the search space. Additionally, it incorporates a majority voting mechanism as a local search strategy to bolster the algorithm's exploitation capabilities. Results from experimentation on eleven datasets underscore the efficiency of MOBDE, outperforming the widely recognized NSGA-II method in terms of the hypervolume (HV) performance metric and minimizing the number of selected features. The proposed algorithm and its experimental outcomes are comprehensively detailed and analyzed, offering valuable insights into its efficacy for feature selection tasks. © 2024 IEEE.

关键词： optimization algorithms

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Integer Ambiguity Resolution Based on Hyperbolic Tangent Iterative Optimizer 11

Integer Ambiguity Resolution Based on Hyperbolic Tangent Ite...

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11th International Forum on Electrical Engineering and Automation, IFEEA 2024

作者： Yu, Xianmiao Wang, Hongru College of Information and Communication Engineering Harbin Engineering University HL Harbin China

ISBN: (纸本)9798331516611

Hyperbolic Tangent Iterative Optimizer (HTIO) algorithm is introduced for ambiguity resolution in double-difference positioning, with reduced complexity and higher success-rate. The DO (De-rounding operation) algorithm is also proposed to address inconsistencies in the geometric properties of search spaces for various ambiguity elements. Applying the HTIO algorithm to ambiguity vectors processed by the DO algorithm significantly enhances the success rate of ambiguity resolution and reduces fixing time. The HTIO algorithm was compared with LAMBDA algorithms and other 6 swarm intelligence optimization algorithms through ambiguity resolution simulation experiment. Comparatively, the HTIO algorithm outperforms the LAMBDA algorithm with a 5.54% increase in search success-rate and a 91.04% reduction in time, and surpasses the IBOA algorithm with a 4.39% increase in success-rate and an 82.14% time reduction. © 2024 IEEE.

关键词： optimization algorithms

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A survey on computation offloading and service placement in fog computing-based IoT

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JOURNAL OF SUPERCOMPUTING 2022年第2期78卷 1983-2014页

作者： Gasmi, Kaouther Dilek, Selma Tosun, Suleyman Ozdemir, Suat Higher Inst Appl Languages & Comp Sci Beja Tunis Tunisia Hacettepe Univ Grad Sch Sci & Engn Ankara Turkey Hacettepe Univ Dept Comp Engn Ankara Turkey

In recent years, fog computing has emerged as a computing paradigm to support the computationally intensive and latency-critical applications for resource limited Internet of Things (IoT) devices. The main feature of fog computing is to push computation, networking, and storage facilities closer to the network edge. This enables IoT user equipment (UE) to profit from the fog computing paradigm by mainly offloading their intensive computation tasks to fog resources. Thus, computation offloading and service placement mechanisms can overcome the resource constraints of IoT devices, and improve the system performance in terms of increasing battery lifetime of UE and reducing the total delay. In this paper, we survey the current research conducted on computation offloading and service placement in fog computing-based IoT in a comparative manner.

关键词： Fog computing Computation offloading Internet of Things (IoT) Service placement optimization algorithms

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A Sparsity Knowledge Transfer-Based Evolutionary Algorithm for Large-Scale Multitasking Multi-Objective optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Wu, Chengming Tian, Ye Zhang, Limiao Xiang, Xiaoshu Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230039 China Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China

Multitasking multi-objective evolutionary algorithms (MMEAs) have been extensively studied in the past decade, which mainly concentrate on multitasking multi-objective optimization problems (MMOPs) with dozens of decision variables. Nevertheless, many real-world MMOPs have thousands of decision variables and are of sparse nature, which are regarded as large-scale MMOPs (LSMMOPs) in this study. To address LSMMOPs, a sparsity knowledge transfer-based evolutionary multi-objective algorithm, termed EMO-SKT, is proposed for efficiently finding high-quality sparse solutions of LSMMOPs. For each target optimization task, a sparsity knowledge transfer strategy extracts sparse distribution information from a source task and incorporates the information into the target task for two types of sparsity knowledge: variable importance and sparse degree. The variable importance is utilized to produce high-quality sparse solutions during the evolutionary search for EMO-SKT, while the sparse degree facilitates the reduction of search space and thus speeds up the convergence of the evolutionary search. Experimental results on eight benchmark problems and six practical LSMMOPs demonstrate the effectiveness of the sparsity knowledge transfer strategy. Furthermore, the proposed EMO-SKT is capable of efficiently finding high-quality sparse solutions on an LSMMOP with over 1000 decision variables. In comparison with five state-of-the-art multitasking or sparse optimization algorithms, the proposed EMO-SKT exhibits superior performance in terms of both solution quality and search efficiency. © 1997-2012 IEEE.

关键词： optimization algorithms

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A Novel Comprehensive Learning JAYA Algorithm Based on Lévy Flights 28

A Novel Comprehensive Learning JAYA Algorithm Based on Lévy...

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28th International Computer Science and Engineering Conference, ICSEC 2024

作者： Shen, Xintong Sunat, Khamron Khon Kaen University College of Computing Khon Kaen Thailand

ISBN: (纸本)9798350366860

To address the issue of the JAYA algorithm becoming stuck in suboptimal solutions, this paper introduces the Lévy flight method and proposes a novel CLJAYA-LF algorithm. This new approach integrates the Lévy flight's "big step"movement strategy with multi-strategy particle updates, which helps particles escape local extrema and restart in new search spaces, thus significantly enhancing exploration capabilities. Additionally, the algorithm utilizes the Lévy flight's "small step"random movement strategy to improve local exploitation and address unidirectional particle progress at specific positions. The CLJAYA-LF algorithm's combination of large and small step movements provides a balanced mechanism for overcoming local optima and refining search efficiency. Experimental results confirm that CLJAYA-LF outperforms existing advanced methods, demonstrating exceptional performance across both low-dimensional and high-dimensional optimization tasks and showing promise for broader applications. © 2024 IEEE.

关键词： optimization algorithms

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An Optimal Computing Budget Allocation Tree Policy for Monte Carlo Tree Search

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第6期67卷 2685-2699页

作者： Li, Yunchuan Fu, Michael C. Xu, Jie Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Maryland Inst Syst Res College Pk MD 20742 USA Univ Maryland RH Smith Sch Business & Inst Syst Res College Pk MD 20742 USA George Mason Univ Dept Syst Engn & Operat Res Fairfax VA 22030 USA

We analyze a tree search problem with an underlying Markov decision process, in which the goal is to identify the best action at the root that achieves the highest cumulative reward. We present a new tree policy that optimally allocates a limited computing budget to maximize a lower bound on the probability of correctly selecting the best action at each node. Compared to widely used upper confidence bound (UCB) tree policies, the new tree policy presents a more balanced approach to manage the exploration and exploitation tradeoff when the sampling budget is limited. Furthermore, UCB assumes that the support of reward distribution is known, whereas our algorithm relaxes this assumption. Numerical experiments demonstrate the efficiency of our algorithm in selecting the best action at the root.

关键词： Search problems Heuristic algorithms Games Monte Carlo methods Machine learning algorithms Dynamic programming Space exploration Machine learning Monte Carlo tree search (MCTS) optimization algorithms stochastic optimal control

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Optimal Acceleration for Minimax and Fixed-Point Problems is Not Unique 41

Optimal Acceleration for Minimax and Fixed-Point Problems is...

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41st International Conference on Machine Learning, ICML 2024

作者： Yoon, TaeHo Kim, Jaeyeon Suh, Jaewook J. Ryu, Ernest K. Department of Mathematical Sciences Seoul National University Korea Republic of Department of Mathematics University of California Los Angeles United States

Recently, accelerated algorithms using the anchoring mechanism for minimax optimization and fixed-point problems have been proposed, and matching complexity lower bounds establish their optimality. In this work, we present the surprising observation that the optimal acceleration mechanism in minimax optimization and fixed-point problems is not unique. Our new algorithms achieve exactly the same worst-case convergence rates as existing anchor-based methods while using materially different acceleration mechanisms. Specifically, these new algorithms are dual to the prior anchor-based accelerated methods in the sense of H-duality. This finding opens a new avenue of research on accelerated algorithms since we now have a family of methods that empirically exhibit varied characteristics while having the same optimal worst-case guarantee. Copyright 2024 by the author(s)

关键词： optimization algorithms

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A Modified Wei-Yao-Liu Stochastic Conjugate Gradient Algorithm in Machine Learning

A Modified Wei-Yao-Liu Stochastic Conjugate Gradient Algorit...

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2024 International Conference on Computational Intelligence and Communication System, CCICS 2024

作者： Mo, Zhongyu Yuan, Gonglin Deng, Wei Guangxi University Guangxi Nanning China School of Finance and Economics Nanning College for Vocational Technology Guangxi Nanning China

The Wei-Yao-Liu (WYL) Conjugate Gradient (CG) algorithm exhibits favourable attributes, notably sufficient descent and trust domain characteristics, in the context of solving unconstrained optimization problems. The exploration and enhancement of computational methodologies for stochastic optimization problems have garnered significant attention. In this study, our objective is to improve the Wei-Yao-Liu (WYL) CG algorithm and introduce an effective optimization method tailored for solving stochastic optimization problems. We will present a comprehensive framework for analysing the convergence of this algorithm and, under suitable conditions, establish its sufficient descent properties, trust region features, and global convergence of stationary points. We will also give an improved CG algorithm to calculate a framework for variance reduction. Additionally, we will conduct experimental comparisons to showcase the competitiveness of our proposed algorithm. © Published under licence by IOP Publishing Ltd.

关键词： optimization algorithms

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