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An adaptive search strategy combination algorithm based on reinforcement learning and neighborhood search

JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第2期12卷 177-217页

作者： Liu, Xiaotong Xu, Ying Wang, Tianlei Zeng, Zhiqiang Zhou, Zhiheng Zhai, Yikui Wuyi Univ Sch Mech & Automat Engn Jiangmen 529020 Peoples R China Jiangmen Key Lab Kejie Semicond Bonding Technol & Jiangmen 529020 Peoples R China Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China South China Univ Technol Coll Future Technol Guangzhou 510640 Peoples R China

Integrating multiple search operators to utilize their different characteristics in order to improve the performance of evolutionary algorithms is a challenging task. This paper proposes an adaptive combination algorithm that integrates four search operators, called RLACA. RLACA introduces a reinforcement learning-based adaptive search operator selection mechanism (RLAS) to dynamically choose the most suitable search operator based on the individual states. Additionally, a neighborhood search strategy based on differential evolution (NSDE) is incorporated to mitigate premature convergence by increasing population diversity. To verify the effectiveness of the proposed algorithm, a comprehensive testing was conducted using the CEC2017 test suite. The experimental results demonstrate that RLAS can adaptively select a suitable search operator and NSDE can enhance the algorithm's local search capability, thereby improving the performance of RLACA. Compared with the four basic algorithms and four combination algorithms, RLACA performs better in both convergence speed and resolution accuracy.

关键词： reinforcement learning combination algorithm evolutionary algorithm swarm intelligence

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Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis

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Chinese Journal of Aeronautics 2024年第12期37卷 258-274页

作者： Xiukai YUAN Weiming ZHENG Yunfei SHU Yiwei DONG School of Aerospace Engineering Xiamen UniversityXiamen 361005China

Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters *** estimate the Imprecise Time-variant Failure Probability Function(ITFPF)and derive the imprecise reliability results as a byproduct,Adaptive combination Augmented Line Sampling(ACALS)is *** consists of three integrated features:Augmented Line Sampling(ALS),adaptive strategy,and the optimal *** is adopted as an efficient analysis tool to obtain the failure probability function *** ***,the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time ***,the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance(C.o.V.)of the ITFPF ***,the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal ***,the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering *** examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.

关键词： Time-variant reliability Imprecise reliability Line sampling Adaptive strategy combination algorithm

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High accuracy combination algorithm and a posteriori error estimation for solving the first kind Abel integral equations

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APPLIED MATHEMATICS AND COMPUTATION 2006年第2期178卷 441-451页

作者： Liu, Ya-Ping Tao, Lu Sichuan Univ Coll Math Chengdu 610064 Sichuan Peoples R China

This paper presents high accuracy combination algorithm for solving the first kind Abel integral equations. To avoid solving ill-posed problems, we transform the first kind Abel integral equation to the second kind Volterra integral equation with a continuous kernel expressed by a weakly singular integral. By using integration rules the approximation of this kernel can be easily computed. Then two quadrature algorithms for solving Abel integral equations are proposed, which possess accuracy order O(h(1+alpha))(0 < alpha < 1) and asymptotic expansion of the errors. By means of combination algorithm, we may obtain an approximate solution with a higher accuracy order 0(h 2). Moreover a posteriori error estimate for the algorithms is derived. Both theory and numerical examples show that it is effective and saves storage capacity and computational work. (c) 2005 Elsevier Inc. All rights reserved.

关键词： Abel integral equation of the first kind quadrature method asymptotic expansion combination algorithm

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A combination algorithm of Multiple Lattice-Valued Concept Lattices

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第5期6卷 881-892页

作者： Yang, Li Wang, Yuhui Xu, Yang North China Univ Water Resources & Elect Power Sch Math & Informat Sci Zhengzhou 450046 Henan Peoples R China Southwest Jiaotong Univ Coll Traff & Transportat Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Intelligent Control Dev Ctr Chengdu 610031 Sichuan Peoples R China

With the rapid development of network technology, especially in the internet area, distributed computation and parallel processing of data are urgently needed. This paper studies the combination operation of the lattice-valued concept lattices to realize its distributed computation and parallel processing. The proposal of its corresponding combination algorithm is mainly according to the conjunction properties of incomparable attribute values. Firstly, we define a homotypic lattice-valued concept lattice and a combined concept lattice;secondly, we analyze some relations between the lattice-valued formal concepts under two conditions and prove the isomorphism of the combination theory;finally, we present the combination algorithm of multiple lattice-valued concept lattices and analyze the algorithm complexity and employ an example to show the application of this combination algorithm.

关键词： Concept lattice Lattice-valued concept lattice Homotypic formal context combination algorithm Parallel processing

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Measured Scan Data Reduction combination algorithm Based on Scan Line 3

Measured Scan Data Reduction Combination Algorithm Based on ...

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3rd International Conference on Advanced Engineering Materials and Architecture Science (ICAEMAS)

作者： Wang, Shigang Yan, Yong Wang, Fengjuan Qiqihar Univ Sch Mechatron Engn Qiqihar 161006 Peoples R China

ISBN: (纸本)9783038352679

The 3D laser scanning technology is a hot spot in developed measuring in recent years. In the surface reconstruction of reverse engineering, the 3D laser scanning point cloud data is too large, and is not conducive to the computation, storage and surface reconstruction. After understanding the research status of the point cloud data processing streamline method at home and abroad, and through the analysis of minimum distance algorithm and the angle-chord height combined code method applicable to engineering characteristics, at the same time, the combination algorithm, which is based on the minimum distance algorithm and the angle-chord height combined code method, is proposed to simplify the point cloud data. The scanning point cloud is simplified by using matlab line by line.

关键词： Reverse Engineering Laser Scanning Data Reduction combination algorithm

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A combination algorithm for Image Denoising and Deblurring

A Combination Algorithm for Image Denoising and Deblurring

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Xu, Yiping Chen, Hanlin Zheng, Kelong SW Univ Sci & Technol Sch Sci Mianyang Sichuan Peoples R China

ISBN: (纸本)9781424437092

In this paper, we propose an efficient combination algorithm of split Bregman method, algebraic multigrid method and Krylov acceleration method for image denoising and deblurring. The split Bregman method is used to convert nonlinear TV model into linear systems. The linear systems with the blur operator are difficult for solving. We apply an algebraic multigrid (AMG) method and Krylov acceleration method to solve the linear systems in the inner iteration. For the linear systems, we have added an auxiliary linear term that significantly stabilizes and accelerates the outer iteration. The inclusion of the linear stabilizing term plays a crucial role in our algorithm. We have also conducted numerical experiments. The results show that our algorithm is efficient and robust. The results of numerical experiments are given and compared with ones of [6], [11].

关键词： Krylov acceleration method algebraic multigrid method auxiliary linear term blur operator combination algorithm differential equations image deblurring image denoising image denoising image restoration inner iteration linear systems nonlinear TV model outer iteration split Bregman method

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Study on Seismic Magnitude Prediction Based on combination algorithm 9th

Study on Seismic Magnitude Prediction Based on Combination A...

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9th International Conference on Modelling, Identification and Control (ICMIC)

作者： Zhou, Wan-zhen Kan, Jing-sen Sun, Shuo Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050000 Hebei Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Shandong Peoples R China

ISBN: (纸本)9781509065738

Earthquake as a kind of natural disaster, it has a great destruction, but the magnitude of earthquake is closely related to the latitude, longitude and the depth of focal. It is very important to realize the magnitude prediction by data mining. In order to reduce the damage degree of earthquake to human's life and enhance the prediction ability of magnitude at different sites and depth of source, the support vector machine (SVM) and neural network algorithm are used to construct the magnitude prediction model based on existing seismic data, and the prediction of possible earthquake magnitude at different locations and focal depth is realized based on combination algorithm of support vector machine and neural network. Experiment results shows that the predictive ability of combination algorithm is better than using traditional SVM or neural network obviously, and have large extent overcome the disadvantage of support vector machine in solving multi-classification problems and bad selection of artificial neural network parameters easily lead to over-fitting or under-fitting and other disadvantages.

关键词： Data mining combination algorithm support vector machine neural network magnitude prediction

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Base Model combination algorithm for Resolving Tied Predictions for K-Nearest Neighbor OVA Ensemble Models

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INFORMS JOURNAL ON COMPUTING 2013年第3期25卷 517-526页

作者： Lutu, Patricia E. N. Engelbrecht, Andries P. Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

Model aggregation is the process of constructing several base models that are then combined into a single model for prediction. Ensemble classification has been studied by many researchers and found to provide significant performance improvements over single models. This paper presents a new base model combination algorithm for K-nearest neighbor (KNN) ensemble models based on One-Versus-All (OVA) classification. The proposed algorithm uses two decision functions to determine the best prediction among the many predictions provided by the base models. It is demonstrated in this paper that tied or conflicting predictions can be effectively resolved when a probabilistic function and a distance function are used by a combination algorithm for OVA KNN base model predictions. The resolution of tied predictions leads to improvements in predictive performance.

关键词： ensemble classification model aggregation combination algorithm OVA classification K-nearest neighbor classification predictive data mining data mining

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A Novel combination algorithm of Different Methods of Maximum Power Point Tracking for Grid-Connected Photovoltaic Systems

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JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME 2021年第4期143卷 041003页

作者： Ali, Mahmoud N. Benha Univ Fac Engn Dept Elect Engn 108 Shoubra St Cairo Egypt

This paper proposes a novel algorithm to combine two or more techniques of maximum power point tracking (MPPT) to increase the output dc power and the efficiency of photovoltaic (PV) arrays. Different MPPT methods have dissimilar responses for the same environmental circumstances. The combination of multiple methods has the advantage of ever acquiring maximum power as the environmental conditions change. The proposed algorithm is used to enhance the selection of the most suitable duty cycle of the combined methods, based on the power-voltage characteristics, to get the best tracking response. Multiple classical and/or artificial intelligence (AI)-based MPPT methods can be combined based on this selection algorithm. To demonstrate its effectiveness, two examples are illustrated. The first one is the combination of two classical MPPT methods, which are the incremental conductance (INC) and the perturb-and-observe (P&O). The second example is to combine two AI-based MPPT, which are the artificial neural network (ANN) and the fuzzy logic control (FLC). The simulation results of the application of the proposed algorithm to a grid-connected PV system model justify its capability to acquire better static and dynamic responses than the responses of individual MPPT methods.

关键词： maximum power point tracking combination algorithm artificial neural network fuzzy logic incremental conductance perturb-and-observe grid-connected PV systems

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Global failure probability function estimation based on an adaptive strategy and combination algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年 231卷

作者： Yuan, Xiukai Qian, Yugeng Chen, Jingqiang Faes, Matthias G. R. Valdebenito, Marcos A. Beer, Michael Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China TU Dortmund Univ Chair Reliabil Engn Leonhard Euler Str 5 D-44227 Dortmund Germany Leibniz Univ Hannover Inst Risk & Reliabil Callinstr 34 Hannover Germany Univ Liverpool Inst Risk & Uncertainty Peach St Liverpool L69 7ZF England Tongji Univ Int Joint Res Ctr Resilient Infrastruct Shanghai 200092 Peoples R China Tongji Univ Int Joint Res Ctr Engn Reliabil & Stochast Mech Shanghai 200092 Peoples R China

The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables of a reliability problem. Knowledge on this FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. However, its calculation is usually a challenging task. Therefore, this paper presents an efficient approach for estimating the FPF based on an adaptive strategy and a combination algorithm. The proposed approach involves three basic elements: (1) a Weighted Importance Sampling approach, which allows determining local FPF estimates;(2) an adaptive strategy for determining at which realisations of the distribution parameters it is necessary to perform local FPF estimation;and (3) an optimal combination algorithm, which allows to aggregate local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach.

关键词： Failure probability function Importance sampling combination algorithm Adaptive strategy

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