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Short-Term Wind Power Prediction Based On Particle Swarm optimization-Extreme Learning Machine Model Combined With Adaboost algorithm

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IEEE ACCESS 2021年 9卷 94040-94052页

作者： An, Guoqing Jiang, Ziyao Cao, Xin Liang, Yufei Zhao, Yuyang Li, Zheng Dong, Weichao Sun, Hexu Hebei Univ Sci & Technol Sch Elect Engn Shijiazhuang 050018 Hebei Peoples R China Hebei Engn Lab Wind Power Photovolta Coupling Hyd Shijiazhuang 050018 Hebei Peoples R China Hebei Construct & Investment Grp New Energy Co Lt Shijiazhuang 050051 Hebei Peoples R China

As the proportion of wind power in the world's electricity generation increases, improving wind power prediction accuracy is vital for making full use of wind energy and ensuring the safe and stable operation of the power grid. Given the uncertainty and volatility of wind power and the weak generalization ability of the current wind power prediction models, we propose a wind power prediction model that combines Adaboost algorithm with extreme learning machine optimized by particle swarm optimization (PSO-ELM). First, particle swarm optimization is used to optimize the initial thresholds and input weights of the ELM to obtain the PSO-ELM basic prediction model. Then, combined with the Adaboost algorithm, a series of PSO-ELM weak predictors with input weights and thresholds optimized by PSO and containing different hidden layer nodes are composed. Finally, each weak predictor is weighted and fused into a strong prediction model of wind power, and the final prediction results are output. In this paper, the Adaboost-PSO-ELM model is verified by a wind turbine's measured data in Turkey. The prediction indicators are compared with the current wind power prediction methods including optimized neural networks and ensemble learning models. The results show that the Adaboost-PSO-ELM wind power prediction model has higher accuracy and better generalization ability.

关键词： Adaboost algorithm extreme learning machine optimization algorithm wind power prediction

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Cheetah optimization for optimal power management in standalone solar PV systems with EV integration

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ENGINEERING RESEARCH EXPRESS 2025年第2期7卷 025319-025319页

作者： Maurya, Archana Pandey, A. K. MMMUT Dept Elect Engn Gorakhpur India

This research investigates the application of a novel Cheetah optimization (CO) algorithm for optimal load allocation in a standalone solar photovoltaic (PV) system integrated with an electric vehicle (EV) battery. Unlike conventional MPPT techniques like Perturb & Observe, Grey Wolf optimization (GWO), and Flying Squirrel optimization (FSO), the CO algorithm, inspired by the hunting strategies of cheetahs, effectively balances exploration and exploitation of the solution space, leading to improved convergence and optimal load allocation. Simulation studies, validated by hardware-in-the-loop (HIL) testing using Opal-RT, demonstrated the superior performance of CO. CO achieved a 15% higher peak PV power (reaching 1085 W) compared to the best-performing alternative, with a tracking efficiency exceeding 99.997%. Load power delivery was enhanced by 20%, while the CO algorithm optimized battery charging with a 5% faster charging rate and a higher peak State-of-Charge (SOC). Furthermore, CO exhibited improved system stability with a voltage deflection of only 0.65% and allowable battery voltage deviation maintained within 2%. The system demonstrated low Total Harmonic Distortion (THD): 2.12% for PV power, 3.2% for load power, and 1.96% for battery power, significantly lower than other methods. Statistical analysis, including comparisons of mean values and standard deviations, further confirmed the superiority of CO in terms of energy utilization and system stability. These findings highlight the potential of the CO algorithm as a highly effective and promising solution for optimizing load allocation in standalone solar PV systems with EV battery integration, enabling improved energy utilization, reduced reliance on grid power, and enhanced system reliability.

关键词： cheetah optimization MPPT standalone PV EV battery load allocation optimization algorithm energy management

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Hybrid metaheuristic algorithm methods and econometric models in prediction of dogecoin price

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JOURNAL OF MODELLING IN MANAGEMENT 2025年第3期20卷 1030-1080页

作者： Farahani, Milad Shahvaroughi Babaei, Shiva Kharazan, Zahra Sadat Bai, Ali Rahmati, Zahra Ghasemi, Ghazal Alipour, Fardin Farrokhi-Asl, Hamed Khatam Univ Dept Finance Tehran Iran Univ Northern Iowa Coll Business Cedar Falls IA USA Univ Wisconsin Milwaukee Lubar Coll Business Milwaukee WI USA Islamic Azad Univ Dept Law Shahreza Iran Khatam Univ Dept Finance Tehran Iran Univ Wisconsin Milwaukee Cofrin Sch Business Green Bay WI USA

Purpose - This paper aims to predict Dogecoin price by using artificial intelligence (AI) methods and comparing the results with the econometrics models. Design/methodology/approach - An artificial neural network (ANN) was applied as a prediction method without any optimization techniques. Additionally, the genetic algorithm (GA) is used to select the most appropriate input variables. Additionally, based on the literature review and the relationships between cryptoprice and global indices, 20 economic indicators, such as Coinbase Bitcoin, Coinbase Litecoin and US dollars, along with main global stock indices such as FTSE100 and NIFTY50, are identified as input variables for the model. Lichtenberg algorithm (LA) and aquila optimization (AO) algorithm are used to make the ANN more robust. To validate our algorithms, they have been implemented on daily data for the last three years. To demonstrate the superiority of the models over traditional methods such as econometrics, regression analysis and curve fitting techniques are used. The effectiveness of these models is then evaluated and compared using criteria such as recall, accuracy and precision. Findings - The results indicate that AI-based algorithms not only enhance the accuracy, recall and precision of calculations but also expedite the process without requiring the numerous and restrictive assumptions associated with time series and econometric models. Originality/value - The main contribution of this paper is the application of novel approaches such as AO and LA to improve the predictive capabilities of the ANN method for various cryptocurrencies' prices. It demonstrates the superiority of the proposed algorithms over traditional econometric models using real-life data.

关键词： Cryptocurrency Artificial intelligence optimization algorithm Econometric methods Curve-fitting

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An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

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JOURNAL OF COMBINATORIAL optimization 2022年第1期44卷 794-849页

作者： Falahiazar, Alireza Sharifi, Arash Seydi, Vahid Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran

Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.

关键词： Dynamic multi-objective optimization problems Evolutionary algorithm Heating optimization optimization algorithm Spread-based

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NEST: A Quadruple-Node Upset Recovery Latch Design and algorithm-Based Recovery optimization

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 4590-4600页

作者： Huang, Zhengfeng Sun, Liting Wang, Xu Liang, Huaguo Lu, Yingchun Yan, Aibin Pan, Jun Wen, Xiaoqing HeFei Univ Technol Sch Microelect Hefei 230601 Peoples R China Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Fukuoka 8208502 Japan

Multinode upset induced by radiation on integrated circuits has caused many circuit reliability issues. This article proposes a single-event quadruple-node upset (QNU) recovery latch (NEST), based on four circular feedback loops that are formed by 25 C-elements to realize high robustness. NEST achieves 29.02% reduction in power consumption compared to the latch design and algorithm-based verification protected against multiple-node upset (LDAVPM) latch and 51.44% reduction in setup time compared to the quadruple-node upset recoverable and high-impedance-state insensitive latch (QRHIL) latch. NEST also achieves a 99.29% QNU recovery rate. Furthermore, a high-speed, high-precision optimization algorithm for multinode upset recovery is also proposed and implemented. This algorithm achieves 99.84 reduction in simulation time for exhaustive fault injections having equivalent accuracy with high performance simulation program with integrated circuit emphasis (HSPICE).

关键词： Latches Power demand Logic gates Inverters Radiation hardening (electronics) optimization Aerospace and electronic systems optimization algorithm quadruple-node upset (QNU) recovery radiation hardening by design recovery rate

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Cellular gradient algorithm for solving complex mechanical optimization design problems

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2024年 282卷

作者： Wang, Rugui Li, Xinpeng Huang, Haibo Fan, Zhipeng Huang, Fuqiang Zhao, Ningjuan Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China

In mechanical optimization design problems, there are often some non-continuous or non-differentiable objective functions. For these non-continuous and non-differentiable optimization objectives, it is often difficult for existing optimal design algorithms to find the desired optimal solutions. In this paper, we incorporate the idea of gradient descent into cellular automata and propose a Cellular Gradient (CG) method. First, we have given the basic rules and algorithmic framework of CG and designed three kinds of growth and extinction rules respectively. Then, the three evolutionary rules for cellular within a single cycle are analyzed separately for form and ordering. The best expressions for the cellular jealous neighbor rule and the solitary regeneration rule are given, and the most appropriate order in which the rules are run is selected. Finally, the solution results of the cellular gradient algorithm and other classical optimization design algorithms are compared with a multi-objective multiparameter mechanical optimization design problem as an example. The computational results show that the cellular gradient algorithm has an advantage over other algorithms in solving global and dynamic mechanical optimal design problems. The novelty of CG is to provide a new way of thinking for solving optimization problems with global discontinuities.

关键词： Mechanical optimization optimization algorithm Discrete integrable problem Cellular automaton Gradient descent

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Efficient Surrogate Model Assisted Estimation of Distribution algorithm for Expensive optimization

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IEEE ACCESS 2024年 12卷 78248-78260页

作者： Shang, Jin Li, Guiying Hao, Hao Zhang, Yufang Wuxi Inst Technol Inst Control Technol Wuxi 214121 Peoples R China Heilongjiang Univ Sch Mech & Elect Engn Harbin 150080 Heilongjiang Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China

In recent years, several surrogate assisted evolutionary algorithms (SAEAs) have been proposed to solve expensive optimization problems. These problems lack explicit expressions and are characterized by high invocation costs. SAEAs leverage surrogate models to accelerate convergence towards the optimal region and reduce the number of function evaluations. While Gaussian Processes (GPs) are widely used due to their robustness and capability of providing uncertainty estimates, their applicability becomes limited in scenarios involving a large number of samples or high-dimensional spaces. This is due to their cubic time complexity in relation to the number of samples, which results in prohibitive computational demands for large-scale problems. To address the challenge, this work presents an efficient surrogate model-assisted estimation of the distribution algorithm (ESAEDA). This method employs a random forest as a surrogate model and combines it with a GP-hedge acquisition strategy to ensure the efficiency and accuracy of model-assisted selection. An improved EDA model called the variable-width histogram model with some unevaluated solutions is used to generate new solutions. To demonstrate the benefits of the proposed method, we compared ESAEDA with several state-of-the-art surrogate-assisted evaluation algorithms and the Bayesian optimization method. Experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms for two well-known test suites.

关键词： Computational modeling optimization Computational efficiency Closed box Time complexity Heuristic algorithms Gaussian processes Artificial intelligence Random forests optimization algorithm random forest

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Task allocation algorithm and optimization model on edge collaboration

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JOURNAL OF SYSTEMS ARCHITECTURE 2020年 110卷 101778-101778页

作者： Deng, Xiaoheng Li, Jun Liu, Enlu Zhang, Honggang Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Univ Massachusetts Engn Dept Boston MA 02125 USA

This paper investigates a mobile edge computing environment for video analysis tasks where edge nodes provide their computation capacities to process the computation intensive tasks submitted by end users. First, we introduce a Cloudlet Assisted Cooperative Task Assignment (CACTA) system that organizes edge nodes that are geographically close to an end user into a cluster to collaboratively work on the user's tasks. It is challenging for the system to find an optimal strategy that assigns workload to edge nodes to meet the user's optimization goal. To address the challenge, this paper proposes multiple algorithms for different situations. Firstly, considering the situation that historical data cannot be obtained, a multi-round allocation algorithm based on EMA prediction is proposed, and the experimental results prove the efficiency and necessity of multiple rounds of transmission. To address the second case of obtaining historical data, this paper introduces a prediction-based dynamic task assignment algorithm that assigns workload to edge nodes in each time slot based on the prediction of their capacities/costs and an empirical optimal allocation strategy which is learned from an offline optimal solution from historical data. Experimental results demonstrate that our proposed algorithm achieves significantly higher performance than several other algorithms, and especially its performance is very close to that of an offline optimal solution. Finally, we propose an online task assignment algorithm based on Q-learning, which uses the model-free Q-learning algorithm to actively learn the allocation strategy of the system, and the experimental results verify the superiority and effectiveness of this algorithm.

关键词： Edge computing Task assignment Prediction Q-Learning optimization algorithm

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Nonlinear Feature Selection for Multi-target Regression Problems

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SN Computer Science 2025年第5期6卷 1-15页

作者： Kanouté, Mamadou Grall-Maës, Edith Beauseroy, Pierre Computer Science and Digital Society Laboratory (LIST3N) University of Technology of Troyes Troyes France

When designing an inference model, feature selection methods can be used to select a subset of relevant variables from the original set of variables to increase the performance and reduce the complexity of the model. In this work, a new feature selection method exploiting nonlinear relationships between variables is introduced in the context of multi-target regression problems. The proposed approach is based on a neural network with one hidden layer in which a regularization term is introduced to enforce variable selection. Experiments on synthetic and real-world datasets show the effectiveness of the proposed approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mathematical computation Multi-output regression Neural network optimization algorithm Supervised nonlinear feature selection

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Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 537卷 122621-000页

作者： Jiang, Jianhua Yang, Xi Meng, Xianqiu Li, Keqin Jilin Univ Finance & Econ Dept Data Sci Changchun 130117 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Gravitational search algorithm Sine cosine algorithm Balance adjustment mechanism

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