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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Cenikj, Gjorgjina Petelin, Gašper Eftimov, Tome Jozef Stefan International Postgraduate School Computer Systems Department Jožef Stefan Institute Ljubljana Slovenia Jozef Stefan Institute Computer Systems Department Ljubljana Slovenia

ISBN: (纸本)9798400704956

Driven by the success of transformer models in representation learning across different Machine Learning fields, this study explores the development of a transformer-based Algorithm Selection (AS) model. We demonstrate the training of a transformer architecture for AS on a collection of single-objective problem instances generated through a random function generator. The transformer model predicts a numeric performance indicator for all of the algorithms in a given algorithm portfolio, relying solely on raw samples from the objective functions. The transformer model consistently outperforms a single best solver, indicating that AS can be performed without the need for intermediate feature construction. A comparison to the classic Exploratory Landscape Analysis (ELA) approach shows that the transformer model provides comparable results, with the ELA model commonly outperforming the transformer model with differences around 0.02 in terms of the loss metric. © 2024 held by the owner/author(s).

关键词： optimization algorithms

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Clustering Routing Based on Improved Thermal Exchange optimization Algorithm for WSN 12

Clustering Routing Based on Improved Thermal Exchange Optimi...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Yuan, Zhe Wang, Minghua School of Electrical Engineering University of South China Heng Yang China

ISBN: (纸本)9798350355802

Wireless sensor networks are widely used in areas such as monitoring of complex environments. Nodes consume energy due to sensing and transmission, and there is a hot-spot problem. Studies have shown that clustering is an effective method to solve the hot-spot problem. An improved thermal exchange optimization algorithm for selecting cluster heads to improve network QoS based on opposition-based learning and Levy flight mechanisms. By introducing a cluster equalization degree coefficient in the objective function, balanced network energy consumption and reduced network delays. Experimental results show that the proposed algorithm improves the average end-to-end delay of the network by 5%-10% compared to other classical algorithms, and improves the packet delivery rate, the number of packets received at the base station, energy consumption and network lifetime. © 2024 IEEE.

关键词： optimization algorithms

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Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators

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ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2025年 289卷 117628页

作者： Jiang, Shenqiong Cheng, Xiangju Shi, Baoshan Zhu, Dantong Xie, Jun Zhou, Zhihong South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Peoples R China South China Univ Technol State Key Lab Subtrop Bldg & Urban Sci Guangzhou 510640 Peoples R China Chinese Acad Fishery Sci Pearl River Fisheries Res Inst Key Lab Trop & Subtrop Fishery Resource Applicat & Guangzhou 510380 Peoples R China Guangzhou Ecol & Environm Monitoring Ctr Guangdong Guangzhou 510030 Peoples R China

The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable to seek new methods to achieve rapid prediction of antibiotics. Many scholars have shown the effectiveness of machine learning in water quality prediction, however, its effectiveness in predicting antibiotic concentrations in the aquatic environment remains inconclusive. Given that conventional water quality indicators directly or indirectly influence antibiotic concentrations, we explored the feasibility of predicting ciprofloxacin (CFX) concentrations based on conventional water quality indicators with the help of three commonly used machine learning algorithms and two parameter optimization algorithms. Then, we evaluated and determined the best model using four commonly used model performance evaluation metrics. The evaluation results showed that the generalized regression neural network (GRNN) model optimized by particle swarm optimization (PSO) had the best prediction among all the models under the conditions of six input variables, namely COD, NH4+-N, DO, WT, TN, and pH. The performance evaluations were R2= 0.936, NSE= 0.915, RMSE= 3.150 ng/L, and MAPE= 30.909 %. Overall, the CFX prediction models met the requirements for antibiotic concentration prediction accuracy, offering a potential indirect method for predicting antibiotic concentrations in water quality management.

关键词： Machine learning model Water quality prediction optimization algorithms Sensitivity analysis Ciprofloxacin

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An Improved Whale optimization Algorithm for Ocean Wireless Sensor Network Coverage Simulation 7

An Improved Whale Optimization Algorithm for Ocean Wireless ...

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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Yang, Zhongqiang Palaoag, Thelma Domingo University of the Cordilleras Technology and Computer Science Baguio2600 Philippines University of Beibu Gulf College of Electronic and Information Engineering Qinzhou City China

ISBN: (纸本)9798350375107

Wireless sensor network (WSN) is widely used in fields such as marine environmental protection, and their nodes are usually deployed randomly, which can easily lead to low coverage in the monitoring area. On the basis of Whale optimization Algorithm (WOA), a new improved algorithm (IWOA) is proposed to solve the optimization deployment of WSN nodes and improve the communication performance of marine wireless sensor networks. The IWOA algorithm improves the coverage performance of network signals by adjusting the probability values of contraction and spiral update behaviors, as well as dynamically adjusting the step size of whale position updates. Using MATLAB to simulate the signal coverage of wireless sensor networks in a limited area, with 40 sensor nodes, IWOA improves the coverage of WSN by 2.99% compared to WOA. The experimental results show that IWOA algorithm can effectively improve the coverage performance of nodes and enhance the coverage of WSN. © 2024 IEEE.

关键词： optimization algorithms

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Stability Evaluation and optimization Algorithm of Transmission and Distribution System Based on Graph Theory and Complex Network Analysis

Stability Evaluation and Optimization Algorithm of Transmiss...

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2024 International Conference on Telecommunications and Power Electronics, TELEPE 2024

作者： Huang, Mu Rao, Weiting Zhang, Yue Wei, Lin Lin, Shuyi Zhao, Hailing Wang, Yuqi Li, Chaohui Peng, Cheng Danyang power supply branch of State Grid Jiangsu Electric Power Co. Ltd. Jiangsu Danyang212300 China

ISBN: (数字)9798350369212

ISBN: (纸本)9798350369212

Based on the graph theory and the complex network analysis method, the stability of transmission and distribution system (TDS) is deeply studied in this study. Firstly, by establishing the topological structure model of TDS, the connection relationship between system nodes is extracted, and the network characteristics of the system are analyzed by using complex network analysis method. Secondly, aiming at the running state and power flow distribution of the system, the genetic algorithm (GA) optimization method is introduced to optimize the power flow distribution of the system, so as to reduce the power loss of the system and improve the node voltage stability. On this basis, an optimization algorithm based on graph theory and complex network analysis is designed and implemented in this study. By analyzing the topology structure and node characteristics of the system and combining with the optimization algorithm, the optimization of power flow distribution of the system is realized. Finally, the effectiveness and performance advantages of the proposed optimization algorithm are verified by simulation experiments. The research results show that the method proposed in this study can effectively improve the operation efficiency and stability of TDS, and provide new ideas and methods for intelligent operation and management of TDS. © 2024 IEEE.

关键词： optimization algorithms

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A New Multi-objective optimization Algorithm Based on Decomposition with Dynamic Resource Allocation 8

A New Multi-objective Optimization Algorithm Based on Decomp...

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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Zhu, Huazheng Zhang, Jun Jiang, Aiping Liu, Yaping Nie, Zicheng Chongqing University of Science & Technology School of Computer Science and Engineering Chongqing China

ISBN: (纸本)9798331506230

The Dynamic Resource Allocation Multi-Objective optimization Algorithm (MOEA/D-DRA) is a method for solving multi-objective optimization problems (MOPs). This algorithm enhances the performance of the original MOEA/D algorithm through a strategy of dynamically allocating computational resources. However, for some challenging problems, the utility function of MOEA/D-DRA may not effectively allocate resources. This paper proposes an improved MOEA/D-DRA algorithm, introducing a new utility function to more accurately reflect the difficulty of subproblems in the current population, named MOEA/D-DRA-NEF. Experimental comparisons with ten other state-of-the-art MOEA algorithms demonstrate that MOEA/D-DRA-NEF outperforms the other algorithms in terms of convergence and diversity. © 2024 IEEE.

关键词： optimization algorithms

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Tuning-Free Stochastic optimization 41

Tuning-Free Stochastic Optimization

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41st International Conference on Machine Learning, ICML 2024

作者： Khaled, Ahmed Jin, Chi Electrical and Computer Engineering Department Princeton University PrincetonNJ United States

Large-scale machine learning problems make the cost of hyperparameter tuning ever more prohibitive. This creates a need for algorithms that can tune themselves on-the-fly. We formalize the notion of "tuning-free" algorithms that can match the performance of optimally-tuned optimization algorithms up to polylogarithmic factors given only loose hints on the relevant problem parameters. We consider in particular algorithms that can match optimally-tuned Stochastic Gradient Descent (SGD). When the domain of optimization is bounded, we show tuning-free matching of SGD is possible and achieved by several existing algorithms. We prove that for the task of minimizing a convex and smooth or Lipschitz function over an unbounded domain, tuning-free optimization is impossible. We discuss conditions under which tuning-free optimization is possible even over unbounded domains. In particular, we show that the recently proposed DoG and DoWG algorithms are tuning-free when the noise distribution is sufficiently well-behaved. For the task of finding a stationary point of a smooth and potentially nonconvex function, we give a variant of SGD that matches the best-known high-probability convergence rate for tuned SGD at only an additional polylogarithmic cost. However, we also give an impossibility result that shows no algorithm can hope to match the optimal expected convergence rate for tuned SGD with high probability. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Second Order Methods for Bandit optimization and Control 37

Second Order Methods for Bandit Optimization and Control

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37th Annual Conference on Learning Theory, COLT 2024

作者： Suggala, Arun Sun, Y. Jennifer Netrapalli, Praneeth Hazan, Elad Google Research India Princeton University United States Princeton University Google DeepMind United States

Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, existing algorithms achieving these bounds have prohibitive computational costs for high dimensional data. In this paper, we propose a simple and practical BCO algorithm inspired by the online Newton step algorithm. We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex functions that satisfy a condition we call κ-convexity. This class contains a wide range of practically relevant loss functions including linear losses, quadratic losses, and generalized linear models. In addition to optimal regret, this method is the most efficient known algorithm for several well-studied applications including bandit logistic regression. Furthermore, we investigate the adaptation of our second-order bandit algorithm to online convex optimization with memory. We show that for loss functions with a certain affine structure, the extended algorithm attains optimal regret. This leads to an algorithm with optimal regret for bandit LQ problem under a fully adversarial noise model, thereby resolving an open question posed in (Gradu et al., 2020) and (Sun et al., 2023). Finally, we show that the more general problem of BCO with (non-affine) memory is harder. We derive a Ω˜(T2/3) regret lower bound, even under the assumption of smooth and quadratic losses. © 2024 A. Suggala, Y.J. Sun, P. Netrapalli & E. Hazan.

关键词： optimization algorithms

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Optimal Placement and Sizing for Distributed Generation based on an Improved Differential Evolution optimization 1

Optimal Placement and Sizing for Distributed Generation base...

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1st IEEE International Conference on Advanced Power Engineering and Energy, APEE 2024

作者： Sobli, Mohd Ashraf Mohd Sapari, Norazliani Md Hussin, Siti Maherah Yusoff, Khairul Huda Rasid, Madihah Md Said, Dalila Mat Nasir, Syed Norazizul Syed NM7 Services Sdn Bhd Kuala Lumpur Wilayah Persekutuan Malaysia University Technology Malaysia UTM Faculty of Electrical Engineering Johor Bharu Johor Malaysia Management and Science University Faculty of Information Science and Engineering Selangor Malaysia

ISBN: (纸本)9798350360936

Various methods have been developed to optimize Distributed Generation (DG) placement and sizing in the power system network. While better advanced algorithms for DG optimization are being continuously developed, simpler general purpose metaheuristic algorithms that had been developed earlier in the past decade, particularly Differential Evolution (DE), should be reevaluated in term of its application for DG optimization. Despite DE simplicity and significant advancement, DE application in DG optimization study is still minimal. In this paper, an improved differential evolution algorithm for DG optimization for power loss minimization in IEEE-69 radial network has been proposed. And improve the population diversity significantly increase its capability of escaping local optima. The results show the proposed method had 84% success rate, a significant improvement from the other selected DE variants with success rate ranging from 16% to 54%. This finding concludes that the proposed method has high potential of escaping the local optima and consequently can minimize the number of the required runs to find the optimal solution. © 2024 IEEE.

关键词： optimization algorithms

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A Stochastic Quasi-Newton Method for Non-convex optimization with Non-uniform Smoothness 63

A Stochastic Quasi-Newton Method for Non-convex Optimization...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Sun, Zhenyu Wei, Ermin Northwestern University Electrical & Computer Engineering EvanstonIL 60208 United States Northwestern University Electrical & Computer Engineering and Industrial Engineering & Management Science EvanstonIL 60208 United States

ISBN: (纸本)9798350316339

Classical convergence analyses for optimization algorithms rely on the widely-adopted uniform smoothness assumption. However, recent experimental studies have demonstrated that many machine learning problems exhibit nonuniform smoothness, meaning the smoothness factor is a function of the model parameter instead of a universal constant. In particular, it has been observed that the smoothness grows with respect to the gradient norm along the training trajectory. Motivated by this phenomenon, the recently introduced (L0, L1)smoothness is a more general notion, compared to traditional L-smoothness, that captures such positive relationship between smoothness and gradient norm. Under this type of non-uniform smoothness, existing literature has designed stochastic first-order algorithms by utilizing gradient clipping techniques to obtain the optimal O(-3) sample complexity for finding an -approximate first-order stationary solution. Nevertheless, the studies of quasiNewton methods are still lacking. Considering higher accuracy and more robustness for quasi-Newton methods, in this paper we propose a fast stochastic quasi-Newton method when there exists non-uniformity in smoothness. Leveraging gradient clipping and variance reduction, our algorithm can achieve the best-known O(-3) sample complexity and enjoys convergence speedup with simple hyperparameter tuning. Our numerical experiments show that our proposed algorithm outperforms the state-of-the-art approaches. © 2024 IEEE.

关键词： optimization algorithms

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