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检索条件"主题词=Optimization algorithms"

共 4003 条记录，以下是751-760 订阅

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level optimization 27

A First-Order Multi-Gradient Algorithm for Multi-Objective B...

引用

27th European Conference on Artificial Intelligence, ECAI 2024

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781643685489

In this paper, we study the Multi-Objective Bi-Level optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. © 2024 The Authors.

关键词： optimization algorithms

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optimization of Coupled ARX-Laguerre Multimodel Parameters Using Genetic Algorithm 12

Optimization of Coupled ARX-Laguerre Multimodel Parameters U...

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2024 12th International Conference on Systems and Control, ICSC 2024

作者： Benamor, Hajer Yousfi, Marwa Alaoui, Larbi Chrifi Garna, Tarek Monastir5019 Tunisia Amiens80000 France Higher Institute of Applied Science and Technology of Sousse Sousse4003 Tunisia

ISBN: (纸本)9798331531812

This work treats parametric optimization of nonlinear systems using genetic algorithms. The authors focus, in this paper, in combining the ARX-Laguerre model and the multimodel approach with its coupled configuration to develop a model named the coupled ARX-Laguerre multimodel. The proposed model is characterized by its Laguerre poles, Fourier coefficients and weighting functions parameters. The complexity of the model is tightly related to the optimal identification of the Laguerre poles characterizing the model. The authors propose an optimization algorithm of the multimodel parameters (Laguerre poles and weighting functions parameters) based on the genetic to guarantee a significant parametric number reduction with regards to the classic ARX multimodel. © 2024 IEEE.

关键词： optimization algorithms

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Projection-Free Variance Reduction Methods for Stochastic Constrained Multi-Level Compositional optimization 41

Projection-Free Variance Reduction Methods for Stochastic Co...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Jiang, Wei Yang, Sifan Yang, Wenhao Wang, Yibo Wan, Yuanyu Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence Nanjing University Nanjing China School of Software Technology Zhejiang University Ningbo China

This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set is closed and convex. Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings;2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives. To address these issues, we introduce novel projection-free variance reduction algorithms and analyze their complexities under different criteria. For gradient mapping, our complexities improve existing results and match the optimal rates for unconstrained problems. For the widely-used Frank-Wolfe gap criterion, we provide theoretical guarantees that align with those for single-level problems. Additionally, by using a stage-wise adaptation, we further obtain complexities for convex and strongly convex functions. Finally, numerical experiments on different tasks demonstrate the effectiveness of our methods. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Oversampling-Guided Search for Evolutionary Multiobjective optimization 13

Oversampling-Guided Search for Evolutionary Multiobjective O...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kimoto, Norihiro Horaguchi, Yuma Nakata, Masaya Collage of Engineering Science Yokohama National University Yokohama Japan

ISBN: (纸本)9798350308365

This paper proposes an oversampling-guided search framework to improve the search efficiency of multiobjective evolutionary algorithms (MOEAs) for high-dimensional prob-lems. The proposed algorithm uses an oversampling algorithm to generate artificial samples of obtained Pareto solutions, and then it uses those as parent solutions to guide the evolutionary search to good subspaces that have not been sufficiently explored. We in-corporate the proposed algorithm into two popular MOEAs, i.e., NSGA-II and IBEA. Experimental results show that the proposed algorithm significantly improves the performance of NSGA-II and IBEA on problems with up to 200 decision variables. We also discuss a possible direction for further improvement while demonstrating the drawbacks of using oversampling techniques. © 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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Cooperation Algorithm Based on Bandwidth Exchanging and Power optimization in Wireless Networks 5

Cooperation Algorithm Based on Bandwidth Exchanging and Powe...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Wang, Ting Sun, Tianle Zhang, Chuang Zang, Yuxia College of Electric and Information Engineering Heilongjiang University of Science and Technology Harbin China Harbin Research Institute of Electrical Instruments Harbin China

ISBN: (纸本)9798350386943

The research of traditional cooperation communication is based on the assumption that the nodes are willing to cooperate, but in fact the selfish nodes are not willing to cooperate in order to save their own resources. To promote the selfish nodes of wireless networks to participate in cooperation, a cooperation algorithm based on bandwidth exchanging and power optimization is proposed. In the first stage of the algorithm, we proved that the optimal cooperation bandwidth allocation is a Nash Bargaining game whose solution can be obtained by using Lagrange multiplier method. In the second stage of the algorithm, after obtaining the cooperation bandwidth allocation, the nodes determine their optimal transmit power though particle swarm optimization. Simulation results show that the proposed algorithm can significantly improve the energy efficiency of the nodes compared to the DT strategy, and the energy efficiency of the system can reach the optimum compared to fixed power strategy. Meanwhile, the nodes can quickly converge to the optimal transmit power. © 2024 IEEE.

关键词： optimization algorithms

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A multi-stage active distribution network coordination planning method based on adaptive particle swarm optimization algorithm 3

A multi-stage active distribution network coordination plann...

引用

3rd International Conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Lin, Huaide Zhang, Zhaolong Nie, Dandan Guangdong Power Grid Co. Ltd. Foshan Power Supply Bureau Guangdong Foshan528000 China Central Southern China Electric Power Design Institute Co.Ltd. China Power Engineering Consulting Group Hubei Wuhan430071 China

ISBN: (纸本)9798350352658

In order to solve the problems of low efficiency and high cost in traditional distribution network coordination planning, a multi-stage active distribution network coordination planning method based on adaptive particle swarm optimization algorithm is proposed. This method first collects abnormal features of multi-stage active distribution networks, and then constructs distribution network information transmission channels based on the collected results. Secondly, based on the adaptive particle swarm optimization algorithm, abnormal features of the distribution network are extracted, and the extracted results are used as input for distribution network anomaly detection. Based on the detection results, coordinated planning of multi-stage active distribution networks is achieved. The experimental results show that this method can effectively improve the efficiency of distribution network planning and reduce operating costs. This method provides an effective solution to enhance the intelligence level of distribution network planning and promote the development of active distribution networks. © 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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Evolutionary Multi - Modal optimization Using Persistence-Based Clustering in Riemannian Manifolds 13

Evolutionary Multi - Modal Optimization Using Persistence-Ba...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Meng, Xiang Pei, Yan Takagi, Hideyuki Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu965-8580 Japan University of Aizu Computer Science Division Aizuwakamatsu965-8580 Japan Kyushu University Fukuoka815-8540 Japan

ISBN: (纸本)9798350308365

This paper presents an innovative approach employing persistence-based clustering in Riemannian manifolds within evolutionary computation algorithms to address multi-modal optimization problems. The proposed framework is im-plemented and evaluated using the chaotic evolution algorithm. We introduce a novel algorithm named chaotic evolution with a clustering algorithm (CECA), which integrates the chaotic evolution characteristics from chaotic systems with the clustering method and Gaussian local search to solve multi-modal optimization problems. By leveraging chaotic dynamics, CECA enhances exploration and exploitation for efficient searching. Simultane-ously, it utilizes the clustering method to improve population diversity in the context of multi-modal optimization problems. The effectiveness and advantages of the proposed framework on the CECA algorithm are demonstrated through extensive experimental evaluations of various benchmark functions, in-cluding the Congress on Evolutionary Computation (CEC) con-ference functions. The experimental results indicate that the proposed framework exhibits distinct advantages in optimizing high-dimensional complex multi-modal functions. This study provides empirical evidence that persistence-based clustering in Riemannian manifolds constitutes an effective methodology for evolutionary multi-modal optimization. © 2024 IEEE.

关键词： optimization algorithms

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Quasi-oppositional-Based Whale optimization Applied to Multilevel Image Thresholding 7th

Quasi-oppositional-Based Whale Optimization Applied to Multi...

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7th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Chakraborty, Falguni Sarkar, Tushnik Roy, Provas Kumar Guha, Debasis Dr. B. C Roy Engineering College West Bengal Durgapur India Kalyani Government Engineering College West Bengal Kalyani India

ISBN: (纸本)9789819720880

Due to the incorrect proportions between the exploitation and exploration phases, the whale optimization algorithm (WOA) gets stuck into the local optima, which causes premature convergence. To address this issue, quasi-oppositional-based learning is imposed with a whale optimization algorithm called quasi-oppositional WOA (QOWOA) to improve the local search ability. Also, it enhances the possibility of producing excellent individuals in the random search stage of the algorithm. To verify the performance of this QOWOA, Otsu’s and Kapur’s entropy were used as the objective function to segment the different regions of images using multilevel threshold values on some grayscale images. The simulation and analysis results of different quality metrics validate the superiority and robustness of the proposed algorithm over the basic WOA algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

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