检索结果-内蒙古大学图书馆

A hybridized approach for minimizing energy in cloud computing

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 53-70页

作者： Kak, Sanna Mehraj Agarwal, Parul Alam, M. Afshar Siddiqui, Farheen Jamia Hamdard Dept Comp Sci & Engn Hamdard Nagar New Delhi 110062 India

Traditional scheduling techniques are designed to reduce processing times while disregarding energy costs. One way of lowering energy usage is to implement scheduling strategies that distribute tasks to specified resources, which influence the processing time and power usage. Among the primary objectives to be achieved in cloud computing, power and energy consumption of the cloud environment have become issues due to ecological and economic reasons. Despite the existence of research efforts from the past pertaining to the same topic, an ideal solution to this problem has not yet been found. One of the main drawbacks of utilizing cloud computing is that the cloud data centers hosting cloud computing applications use higher volumes of energy, adding to the increased functioning cost and carbon footprint in the environment, which in turn increases the need for energy-efficient systems. In this research work, a hybrid optimization algorithm is presented, which is intended to minimize energy in the cloud computing environment. Thus, the crow search algorithm (CSA) and the sparrow search algorithm (SSA) are combined to obtain the proposed hybrid model. The hybrid approach achieves the optimal position in the shortest period of time and with the least amount of energy, load, and makespan, hence improving system performance. The experiments were conducted using three setups with different task sizes. The analysis while using the task size = 300 shows that the proposed method improved QoS, Resource Utilization (RU) and decreased makespan, energy usage, and load at a rate of 0.5073, 4.4035, 0.0331, and 0.0014, respectively.

关键词： Carbon footprint Cloud service provider Crow search algorithm Dynamic scheduling Energy consumption Energy-aware scheduling evolutionary algorithms Green cloud computing Load Makespan Multi-heuristics Multi-cloud Optimization Quality of service Resource allocation Resource utilization Sparrow search algorithm Sustainable Task scheduling

来源：评论

学校读者我要写书评

暂无评论

Enhancing Feature Selection in High-Dimensional Data With Fuzzy Fitness-Integrated Memetic algorithms

引用

IEEE ACCESS 2024年 12卷 130675-130692页

作者： Reddy, Keerthi Gabbi Mishra, Deepasikha VIT AP Univ Sch Comp Sci & Engn Amaravati 522237 Andhra Pradesh India

In environments rich in data, machine learning models often encounter challenges such as data sparsity and overfitting, primarily due to datasets with an excessive number of features. To address these issues, this paper introduces a novel feature selection method employing a Memetic Algorithm (MA) enhanced with a fuzzy fitness function. This method is articulated in three variations: the Fuzzy Fitness Memetic Algorithm with Tabu Search (FFMATS), the Fuzzy Fitness Memetic Algorithm with Hill Climbing (FFMAHC), and a hybrid that combines both techniques, each utilizing specific local search strategies to refine feature selection. When tested across 16 UCI datasets using four different classifiers, these algorithms not only demonstrated competitive accuracy but frequently outperformed existing methods. These results highlight the critical importance of customizing feature selection strategies to meet the specific needs of various datasets and classifiers, ultimately enhancing the practicality and effectiveness of machine learning models.

关键词： Classification algorithms Rough sets Machine learning algorithms Genetic algorithms Feature extraction Uncertainty evolutionary computation Memetics evolutionary algorithms feature selection fuzzy fitness functions local search algorithms machine learning memetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Delta-NAS: Difference of Architecture Encoding for Predictor-Based evolutionary Neural Architecture Search

Delta-NAS: Difference of Architecture Encoding for Predictor...

引用

2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Sridhar, Arjun Chen, Yiran Duke University DurhamNC United States

ISBN: (纸本)9798331510831

Neural Architecture Search (NAS) continues to serve a key roll in the design and development of neural networks for task specific deployment. Modern NAS techniques struggle to deal with ever increasing search space complexity and compute cost constraints. Existing approaches can be categorized into two buckets: fine-grained computational expensive NAS and coarse-grained low cost NAS. Our ob-jective is to craft an algorithm with the capability to per-form fine-grain NAS at a low cost. We propose projecting the problem to a lower dimensional space through pre-dicting the difference in accuracy of a pair of similar net-works. This paradigm shift allows for reducing computational complexity from exponential down to linear with re-spect to the size of the search space. We present a strong mathematical foundation for our algorithm in addition to extensive experimental results across a host of common NAS Benchmarks. Our methods significantly out performs ex-isting works achieving better performance coupled with a significantly higher sample efficiency. © 2025 IEEE.

关键词： evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

evolutionary Variational Optimization of Generative Models

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-51页

作者： Drefs, Jakob Guiraud, Enrico Luecke, Joerg Carl von Ossietzky Univ Oldenburg Machine Learning Lab D-26129 Oldenburg Germany CERN CH-1211 Geneva Switzerland

We combine two popular optimization approaches to derive learning algorithms for generative models: variational optimization and evolutionary algorithms. The combination is realized for generative models with discrete latents by using truncated posteriors as the family of variational distributions. The variational parameters of truncated posteriors are sets of latent states. By interpreting these states as genomes of individuals and by using the variational lower bound to define a fitness, we can apply evolutionary algorithms to realize the variational loop. The used variational distributions are very flexible and we show that evolutionary algorithms can effectively and efficiently optimize the variational bound. Furthermore, the variational loop is generally applicable ("black box") with no analytical derivations required. To show general applicability, we apply the approach to three generative models (we use Noisy-OR Bayes Nets, Binary Sparse Coding, and Spike-and-Slab Sparse Coding). To demonstrate effectiveness and efficiency of the novel variational approach, we use the standard competitive benchmarks of image denoising and inpainting. The benchmarks allow quantitative comparisons to a wide range of methods including probabilistic approaches, deep deterministic and generative networks, and non-local image processing methods. In the category of "zero-shot" learning (when only the corrupted image is used for training), we observed the evolutionary variational algorithm to significantly improve the state-of-the-art in many benchmark settings. For one well-known inpainting benchmark, we also observed state-of-the-art performance across all categories of algorithms although we only train on the corrupted image. In general, our investigations highlight the importance of research on optimization methods for generative models to achieve performance improvements.

关键词： Expectation Maximization Variational Methods evolutionary algorithms Sparse Coding Denoising Inpainting

来源：评论

学校读者我要写书评

暂无评论

Multi-objective optimization of portfolio selection involving non-convex attributes in an anti-fragile perspective

引用

EVOLVING SYSTEMS 2024年第3期15卷 703-715页

作者： Gotardelo, Davi Goliatt, Leonardo UFRRJ DCAS Ave Pref Alberto Silva Lavinas 1847 BR-25802100 Tres Rios RJ Brazil Univ Fed Juiz de Fora Appl & Computat Mech Dept Rua Jose Lourenco Kelmer S-N BR-36036900 Juiz de Fora MG Brazil

This research aims to expand the portfolio selection horizon beyond the mean and variance metrics derived from the Markowitz model and widely used in CAPM. Although well built theoretically, it is well known that CAPM does not work empirically. Would there be market portfolios higher than the theoretical CAPM market portfolio? This study seeks to answer this question by initially optimizing purely convex attributes. In addition, this study proposes, in a pioneering way, beyond the higher order moments, an antifragile metric, called CVIX, which seeks to evaluate the conditional correlation in relation to the VIX (Volatility Index). Thus, this approach incorporates non-convex attributes through evolutionary algorithms, resulting in an empirical multi-objective optimization proposition involving convex and non-convex attributes. In-sample optimizations were applied in the US market and sample tested from 1994 to 2017. The results indicated that the optimization of purely convex attributes produces worse results than optimizations involving the Sharpe, Omega and the naive portfolio (1/n). On the other hand, tests using the antifragile metric and higher-order attributes presented superior results in all scenarios, indicating that investors can may take other attributes than the mean and variance in the assembly of their portfolios.

关键词： Portfolio selection evolutionary algorithms Antifragility Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Hyperbolic Sine Optimizer: a new metaheuristic algorithm for high performance computing to address computationally intensive tasks

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 6703-6772页

作者： Thapliyal, Shivankur Kumar, Narender Doon Univ Dept Comp Sci Dehra Dun Uttarakhand India

In recent decades, the demand for optimization techniques has grown due to rising complexity in real-world problems. Hence, this work introduces the Hyperbolic Sine Optimizer (HSO), an innovative metaheuristic specifically designed for scientific optimization. Unlike conventional approaches, HSO takes a unique approach by engaging individual members of the population, ensuring a comprehensive exploration of solution spaces. Employing distinctive exploration and exploitation phases, coupled with hyperbolic sinh\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$sinh$$\end{document} function convergence, the optimizer enhances speed, simplify parameter adjustment, alleviates slow convergence, and demonstrates efficiency in high-dimensional optimization. This approach is designed to tackle optimization challenges and enhance adaptability in unpredictable real-world scenarios. The evaluation of HSO's performance unfolds through four distinct testing phases. Initially, a set of 65 widely recognized benchmark functions is employed. These functions cover both unimodal and multi-modal varieties across dimensions of 30, 100, 500, and 1000, including fixed-dimensional functions, to comprehensively assess the exploration, exploitation, local optima avoidance, and convergence capabilities of the proposed algorithm. The results of the HSO algorithm are then compared to those of 15 state-of-the-art metaheuristic algorithms and 8 recently published algorithms. Secondly, HSO's performance is assessed in comparison with the benchmark suite from the Institute of Electrical and Electronics Engineers (IEEE) Congress on evolutionary Computation (CEC). This suite includes 15 benchmark functions for CEC-2015 and an additional 30 benchmark functions for CEC-2017. During the third phase, HSO tackles seven real-world

关键词： Metaheuristics Hyperbolic Sine Optimizer HSO Optimization evolutionary algorithms Nature inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

Optimizing Heterogeneous Vehicle Routes for Urban Distribution Considering the Three-Dimensional Bin Packing Problem of Electric Meters

引用

IEEE ACCESS 2024年 12卷 22003-22031页

作者： He, Zhaolei Zhang, Miaohan Lin, Cong Zhao, Jing Shi, Kun Ai, Zhen Yunnan Power Grid Co Ltd Metering Ctr Kunming 650200 Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming 650500 Peoples R China NARI Nanjing Control Syst Co Ltd Nanjing 211100 Peoples R China

With the establishment of smart grids, there is a growing demand for metering electric meters in urban areas. Given the diverse sizes and delicate nature of these meters, traditional scheduling solutions are unable to cater to the multifaceted requirements of urban environments, meters loading, and subsequent logistics scheduling. This study presents an intelligent scheduling model for electric meters in an urban context, taking into account various constraints such as urban traffic congestion, three-dimensional packing of metering devices, delivery time windows, and heterogeneous vehicles. To solve this, we design an improved whale optimization algorithm using a hybrid multi-phase heuristic approach (IWOA-HMOHA). Simulation results show that compared with the traditional meter logistics scheduling strategy, the IWOA-HMOHA algorithm reduces the objective function by 5.4% similar to 26.1% compared with other similar algorithms. In addition, compared with the traditional first-in-last-out cargo packing method, the vehicle space utilization rate is improved by 12.62%. The proposed models and algorithms demonstrate excellent adaptability to a range of urban constraints, offering valuable insights and a robust framework essential for the development of logistics solutions in urban.

关键词： Logistics applications the three-dimensional bin packing problem hybrid multi-phase heuristic approach evolutionary algorithms difference strategy

来源：评论

学校读者我要写书评

暂无评论

Research on the Most Efficient Use of Wind Energy Resources in the Context of Carbon Neutrality: Overview Based on evolutionary Algorithm

引用

MATHEMATICAL PROBLEMS IN ENGINEERING 2022年第1期2022卷

作者： Zhao, Qianqian Yan, Haowen Jin, Jianglei Lanzhou Univ Sch Econ Lanzhou 730000 Gansu Peoples R China Gansu Inst Publ Adm Gansu Prov Party Comm CPC Party Sch Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Gansu Peoples R China Natl Local Joint Engn Res Ctr Technol & Applicat N Lanzhou 730070 Gansu Peoples R China

An evolutionary algorithm-based optimal allocation method of wind resources under the background of carbon neutralization is proposed in order to better achieve the goal of energy conservation and emission reduction under the background of carbon neutralization, aiming at the current unreasonable allocation of wind resources. The evaluation model of balanced wind resource allocation is designed, and the evaluation index of optimal wind resource allocation is constructed using the evolutionary algorithm. The optimal allocation path of wind energy resources is chosen to achieve the goal of reasonable wind energy resource allocation. Finally, simulation experiments show that using an evolutionary algorithm to solve the problem of poor energy allocation and achieve the research goal, the optimal allocation method of wind energy resources under the background of carbon neutralization can effectively solve the problem of poor energy allocation.

关键词： evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Combining Local Search and Directed Mutation in evolutionary Approaches to 4-Part Harmony 14th

Combining Local Search and Directed Mutation in Evolutiona...

引用

14th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2025, held as part of EvoStar 2025

作者： Pacioni, Elia De Vega, Francisco Fernández Universidad de Extremadura Av. Santa Teresa de Jornet 38 Mérida06800 Spain HES-SO Valais-Wallis Sierre Switzerland

ISBN: (纸本)9783031901669

Artificial Intelligence assisted music composition has gained popularity during the last decade, but still faces problems and difficulties. This paper approaches 4-part harmonization problem in the context of evolutionary computation, a topic that has been discussed for more than forty years but still represents an open challenge. This research proposes local search to improve directed mutation and guide the algorithm towards qualitatively better solutions. Human learning and evolutionary Machine Teaching are also used: data from music conservatory students are exploited to guide the algorithm. The results show that local search significantly improves the quality of the mutation performed. Moreover, a series of longer runs are able to find free error scores. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

An Opposition-Based Learning-Based Search Mechanism for Flying Foxes Optimization Algorithm

引用

Computers, Materials & Continua 2024年第6期79卷 5201-5223页

作者： Chen Zhang Liming Liu Yufei Yang Yu Sun Jiaxu Ning Yu Zhang Changsheng Zhang Ying Guo Software College Northeastern UniversityShenyang110169China School of Information Science and Engineering Shenyang Ligong UniversityShenyang110159China China Telecom Digital Intelligence Technology Co. Ltd.Beijing100035China College of Computer Science and Engineering Ningxia Institute of Science and TechnologyShizuishan753000China

The flying foxes optimization(FFO)algorithm,as a newly introduced metaheuristic algorithm,is inspired by the survival tactics of flying foxes in heat wave *** preferentially selects the best-performing *** tendency will cause the newly generated solution to remain closely tied to the candidate optimal in the search *** address this issue,the paper introduces an opposition-based learning-based search mechanism for FFO algorithm(IFFO).Firstly,this paper introduces niching techniques to improve the survival list method,which not only focuses on the adaptability of individuals but also considers the population’s crowding degree to enhance the global search ***,an initialization strategy of opposition-based learning is used to perturb the initial population and elevate its ***,to verify the superiority of the improved search mechanism,IFFO,FFO and the cutting-edge metaheuristic algorithms are compared and analyzed using a set of test *** results prove that compared with other algorithms,IFFO is characterized by its rapid convergence,precise results and robust stability.

关键词： Flying foxes optimization(FFO)algorithm opposition-based learning niching techniques swarm intelligence metaheuristics evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：