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检索条件"主题词=Optimization algorithms"

共 3635 条记录，以下是751-760 订阅

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Multi-objective Performance optimization of Energy Internet Using Improved NSGA-II 8

Multi-objective Performance Optimization of Energy Internet ...

引用

8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Ming, Yangyang Cao, Junwei Tsinghua University Department of Automation Beijing China Tsinghua University Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9798331523558

In this paper, we propose an improved NSGA-II algorithm to optimize the operation performance of Energy Internet. We designed the corresponding gene structure and related parameters according to the Energy Internet operation demands. Then, to increase the proportion of feasible gene samples, we designed a data sequence compensation function and adopted random compensating with different probability thresholds, which can further increase the performance compared to all samples compensating. Through this means, we obtained the optimal result with better operation indices, which verified the advanced performance of this improved algorithm. In the further research, we will try more system optimal indexes and obtain better results based on the progress of the newest NSGA-like algorithms. © 2024 IEEE.

关键词： optimization algorithms

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Projection-Free Online Convex optimization with Time-Varying Constraints 41

Projection-Free Online Convex Optimization with Time-Varying...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Garber, Dan Kretzu, Ben Technion - Israel Institute of Technology Haifa Israel

We consider the setting of online convex optimization with adversarial time-varying constraints in which actions must be feasible w.r.t. a fixed constraint set, and are also required on average to approximately satisfy additional time-varying constraints. Motivated by scenarios in which the fixed feasible set (hard constraint) is difficult to project on, we consider projection-free algorithms that access this set only through a linear optimization oracle (LOO). We present an algorithm that, on a sequence of length T and using overall T calls to the LOO, guarantees Õ(T3/4) regret w.r.t. the losses and O(T7/8) constraints violation (ignoring all quantities except for T). In particular, these bounds hold w.r.t. any interval of the sequence. This algorithm however also requires access to an oracle for minimizing a strongly convex nonsmooth function over a Euclidean ball. We present a more efficient algorithm that does not require the latter optimization oracle but only first-order access to the time-varying constraints, and achieves similar bounds w.r.t. the entire sequence. We extend the latter to the setting of bandit feedback and obtain similar bounds (as a function of T) in expectation. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Understanding the Adam optimization Algorithm in Machine Learning 2

Understanding the Adam Optimization Algorithm in Machine Lea...

引用

2nd International Workshop on Computer Information Technologies in Industry 4.0, CITI 2024

作者： Hospodarskyy, Oles Martsenyuk, Vasyl Kukharska, Nataliia Hospodarskyy, Andriy Sverstiuk, Sofiia Lviv Polytechnic National Universiy Bandery St. 12 Lviv79001 Ukraine University of Bielsko-Biala Willowa St. 2 Bielsko-Biala43-300 Poland Ternopil National Ivan Puluj Technical University Rus'ka St. 56 Ternopil46001 Ukraine I. Horbachevsky Ternopil National Medical University Maidan Voli 1 Ternopil46002 Ukraine Ternopil National Pedagogical University 2 Maxyma Kryvonosa St. Ternopil46027 Ukraine

Machine learning and artificial intelligence are significant areas of interest in both contemporary science and society. There are various optimization algorithms used. The algorithm's speed depends on the size of the dataset, the number of model parameters, and the number of iterations. Standard gradient descent requires computing the gradient of the cost function over the entire dataset, which can be resource-intensive, especially with large datasets. In Adam, a separate learning rate is maintained for each parameter weight, which is adapted and updated individually. The algorithm selects a smaller learning rate for frequently updated parameters and a larger one for parameters corresponding to rare features. To measure the effectiveness and universality of the Adam, we compared it with other optimization algorithms. Analysis of the experiment results conducted on various datasets, indicates a significant advantage of the Adam optimization algorithm. To make sure our model works well for our specific needs, we made a small dataset ourselves. The famous MNIST dataset, created by American researchers, might not match our handwritten numbers perfectly. The results appear promising, with the model achieving an accuracy of 97%, meaning it correctly predicted 97 out of 100 images. This level of accuracy suggests that the model is performing well on our custom dataset, demonstrating its effectiveness in recognizing and classifying our handwritten numbers. Experiments on various datasets showed that the Adam algorithm is capable of achieving good results across a wide range of machine learning tasks. © 2024 Copyright for this paper by its authors.

关键词： optimization algorithms

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Heterogeneous Multi-UAV Task Allocation Based on Improved Multi-Objective Grey Wolf optimization Algorithm

Heterogeneous Multi-UAV Task Allocation Based on Improved Mu...

引用

2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Liu, Senlin Luo, Rui Wen, Liangdong Zhen, Ziyang He, Jialu Li, Yifan College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Third Test Squadron of Air Force Test Flight Bureau Chengdu China

ISBN: (纸本)9798350384185

With the wide application of UAVs, the scenarios have become more complex, and UAV tasking is facing more *** of all, a heterogeneous multi-UAV collaborative multi-tasking assignment model is constructed, which includes multiple practical constraints such as UAV type, mission type, and timing of mission ***, the optimization objective which includes minimizing total flight distance and mission completion time is set *** addition, traditional multi-objective optimization algorithms cannot effectively search the problem space and generate feasible *** order to solve the model efficiently, an improved multi-objective gray wolf optimization algorithm is proposed in this *** diversity of the population was increased by a hierarchical coding approach and an improved initialization strategy.A competency-based nonlinear parametric control strategy is proposed to improve the convergence of the ***, a learning update strategy based on external archive sets is designed to enhance the search efficiency and further increase the convergence of the ***, it is demonstrated that the improved algorithm can effectively solve the heterogeneous multi-UAV task allocation problem by comparison simulations. © 2024 IEEE.

关键词： optimization algorithms

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Analysis of Mathematical Model and optimization Strategy based on Deep Forest Algorithm

Analysis of Mathematical Model and Optimization Strategy bas...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Fan, Hongjun Qingdao Ocean Shipping Mariners College Qingdao China

ISBN: (纸本)9798350375442

The purpose of this paper is to discuss the mathematical model construction based on Deep Forest algorithm and its optimization strategy. Through detailed analysis of the basic principle, structure and optimization method of the algorithm, a series of improvement measures are proposed to enhance the performance and stability of the algorithm by combining with cases in practical applications. The research results of this paper have important reference value for promoting the application of Deep Forest algorithm in various fields. Through a series of experiments, the original algorithm, deep reinforcement learning optimization, parameter tuning, and comprehensive optimization strategies were evaluated, and the following conclusions were drawn: After combining deep reinforcement learning and parameter tuning strategies, the average accuracy of the algorithm is improved to 0.91, the average recall reaches 0.89, the average F1 value is 0.90, and the computation time is shortened to 1.75 seconds. From the data results, the comprehensive optimization strategy significantly improves the computational efficiency while maintaining high classification performance. The research in this paper provides an important reference value for the application of Deep Forest algorithm in various fields. © 2024 IEEE.

关键词： optimization algorithms

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Situational Awareness optimization Method for Network Information Security Based on Decision Tree Algorithm

Situational Awareness Optimization Method for Network Inform...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Zhang, Xiangfei Jiangsu University of Science and Technology Jiangsu Zhenjiang China

ISBN: (纸本)9798350361537

In view of the limitations of the traditional simulated annealing algorithm in the research of network information security situational awareness optimization method, a research scheme of security situational awareness optimization method based on decision tree algorithm is proposed. Firstly, the influencing factors is accurately located through probability theory, and the indicators is reasonably divided to reduce interference, and the decision tree algorithm is used to construct a research scheme of security situational awareness optimization method. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional simulated annealing algorithm in terms of research accuracy and processing time of influencing factors, and has obvious advantages. The research on security situational awareness optimization method plays an extremely important role in network information, which can accurately predict and optimize the growth characteristics and product generation of network information. However, the traditional simulated annealing algorithm has certain limitations in solving the perceptual optimization simulation problem, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of security situational awareness optimization method based on decision tree algorithm to better solve this problem. In this scheme, the influencing factors is accurately located through probability theory, so as to determine the division of indicators, and the decision tree algorithm is used to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on decision tree algorithm can better solve the limitations of traditional simulation annealing algorithms and improve the simulation accuracy and efficiency in the resear

关键词： optimization algorithms

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From Inverse optimization to Feasibility to ERM 41

From Inverse Optimization to Feasibility to ERM

引用

41st International Conference on Machine Learning, ICML 2024

作者： Mishra, Saurabh Raj, Anant Vaswani, Sharan Simon Fraser University Canada UIUC United States

Inverse optimization involves inferring unknown parameters of an optimization problem from known solutions and is widely used in fields such as transportation, power systems, and healthcare. We study the contextual inverse optimization setting that utilizes additional contextual information to better predict the unknown problem parameters. We focus on contextual inverse linear programming (CILP), addressing the challenges posed by the non-differentiable nature of LPs. For a linear prediction model, we reduce CILP to a convex feasibility problem, allowing the use of standard algorithms such as alternating projections. The resulting algorithm for CILP is equipped with a linear convergence guarantee without additional assumptions such as degeneracy or interpolation. Next, we reduce CILP to empirical risk minimization (ERM) on a smooth, convex loss that satisfies the Polyak-Lojasiewicz condition. This reduction enables the use of scalable first-order optimization methods to solve large non-convex problems while maintaining theoretical guarantees in the convex setting. Subsequently, we use the reduction to ERM to quantify the generalization performance of the proposed algorithm on previously unseen instances. Finally, we experimentally validate our approach on synthetic and real-world problems, and demonstrate improved performance compared to existing methods. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Research on optimization Modeling Methods Combining Genetic Algorithm with Computer Technology 4

Research on Optimization Modeling Methods Combining Genetic ...

引用

4th International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2024

作者： Cai, Ruochun NYU Shanghai Shanghai China

ISBN: (纸本)9798331528676

As an optimization algorithm that imitates the natural selection and genetic mechanism, genetic algorithm applied in the combination with the computer technology, gradually obtained rapid development along with the computer network and information technology. Supported by a comprehensive account of essential elements, the basic theories of genetic algorithm are introduced in this paper, as well as how applications of genetic algorithm have been supported with the use of computer technology. This paper focuses on the optimization modeling method, through the use of genetic algorithm in combination with computer technology, whose efficiency has been validated through case analysis. Aiming at the problem of genetic algorithm, this paper presents an improved genetic algorithm based on variable domain to solve the traveling salesman problem. It finally summarizes the research findings, outlines the future development trends, and underlines that this study opens new directions for the application of genetic algorithms in the field of computer technology, while providing effective technical support for optimization modelling. ©2024 IEEE.

关键词： optimization algorithms

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The Impact of Firefly Algorithm (FA) optimization on Gaussian Kernel-Based Fuzzy C-Means Clustering (GKFCM) Efficiency 10

The Impact of Firefly Algorithm (FA) Optimization on Gaussia...

引用

10th International Conference on Fuzzy Systems and Data Mining, FSDM 2024

作者： Dungkratoke, Narongdech Simtrakankul, Chantana Laomala, Janejira Kaennakham, Sayan Department of Interdisciplinary Science and Internationalization Institute of Science Suranaree University of Technology Nakhon Ratchasima30000 Thailand School of Mathematics and Geoinformatics Institute of Science Suranaree University of Technology Nakhon Ratchasima30000 Thailand

ISBN: (纸本)9781643685694

This study explores the effectiveness of the traditional Firefly algorithm (FA) in optimizing the Gaussian Kernel-based Fuzzy C-means clustering (GKFCM) algorithm by adjusting 'sigma' and 'm'. We compare GKFCM with FA optimization (With FA) to without it (Without FA) using the Calinski Harabasz (CH) index and the number of iterations. For all four datasets analyzed in this study, the findings consistently indicate that the GKFCM algorithm optimized with the Firefly algorithm (FA) performs substantially better than its non-optimized counterpart, achieving higher Calinski Harabasz (CH) scores and requiring fewer iterations across various data types. Results from two initial particle distribution styles confirm FA's robustness in refining clustering outcomes and emphasize its role in enhancing clustering quality and efficiency. © 2024 The Authors.

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Research on UAV Ad Hoc Network Coverage optimization Strategy 24

Research on UAV Ad Hoc Network Coverage Optimization Strateg...

引用

4th International Conference on Artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Wang, Yijun Chen, Jiaqi Zhuang, Dawei School of Electronic Information Engineering Changchun University of Science and Technology China

ISBN: (纸本)9798400710049

In order to improve the coverage of UAV AD hoc network, reduce the loss of UAV in operation and the number of UAV involved, a network coverage optimization algorithm based on improved moth extinguishing algorithm is proposed. Firstly, the artificial potential energy method is applied to avoid obstacles before the global search, secondly, the nonlinear dynamic adaptive step factor is introduced to expand the moth search range at the beginning of the search stage, and finally, the dynamic adjustment strategy of flight direction is integrated to improve the global optimization ability during the search process, and then the optimal solution is modified by Cauchy. The simulation results show that the convergence and stability of the proposed algorithm are superior to the other four algorithms, and the connectivity between UAV nodes is improved, and the coverage rate is 18.32%, 5.48%, 5.09% and 10.6% higher than that of the other four algorithms, respectively, which effectively reduces the loss and the number of UAVs used and reduces the cost. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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