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Grey prediction evolution algorithm with a dominator guidance strategy for solving multi-level image thresholding

APPLIED SOFT COMPUTING 2025年 174卷

作者： Yang, Peixin Hu, Zhongbo Zhou, Yang Su, Qinghua Xiong, Wentao Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China Hubei Engn Univ Sch Math & Stat Xiaogan Hubei Peoples R China

Multi-level thresholding (MLT) stands as a pivotal method for extracting target information from images. Meta-heuristic algorithms provide an efficient way to implement MLT and retains more research space for accuracy optimization of high-dimensional multi-level thresholding (HDMLT) of images than they do for low-dimensional multi-level thresholding (LDMIT). In order to improve the algorithmic accuracy in solving the high-dimensional problems, a grey prediction evolution algorithm with a dominator guidance strategy (GPEdg) is proposed in this paper. GPEdg employs Otsu's method as its objective function to find the best threshold configuration. The novel operator in the algorithm, i.e., a dominator guidance (dg) strategy, uses a linear combination of three difference vectors to guide the top 50% individuals of populations to learn from the top 20% of them. An efficient balance of search abilities suitable for solving HDMLT problems is expected to be achieved by injecting the local search capability of the dg strategy into GPE's powerful global search capability. Furthermore, a thresholding morphological profile based method (TMP) leverages the thresholding results generated by GPEdg to train a support vector machine (SVM) for hyperspectral image classification. Numerical experiments are conducted for the newly proposed algorithm and five state-of-the-art algorithms on three image datasets to compare the performance in six metrics, i.e., peak signal-to-noise ratio, structural similarity index, features similarity index, objective function value, stability and time consumption. Overall accuracy and average accuracy are tested on two commonly used hyperspectral image data. The results show that GPEdg exhibits outstanding thresholding performance while TMP enhances the classification accuracy of these images. If this paper is accepted, Matlab_codes associated with this paper will be uploaded to https://***/Zhongbo-Hu/Prediction-evolutionary-algorithm-HOMEPAGE

关键词： Multi-level image thresholding evolution algorithm Otsu's method Hyperspectral image classification

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A differential evolution algorithm for numerical optimization based on the cosine-exponential population size adaptive method

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MEMETIC COMPUTING 2025年第2期17卷 1-78页

作者： Zeng, Zhiqiang Liang, Wenyi Gao, Le Dongguan Univ Technol Sch Mech Engn Dongguan 523808 Peoples R China Wuyi Univ Sch Elect & Informat Engn Jiangmen 529020 Peoples R China

The performance of differential evolution algorithms is sensitive to the population size, and most existing population size control methods continuously reduce the population during the iteration process, which decreases the exploration ability and makes it difficult to prevent a premature convergence of the algorithm. To improve the exploration ability of the differential evolution algorithm, this paper proposes a cosine-exponential population size adaptive (CEPSA) method. In the iterative process, CEPSA enables the population size to decrease or increase. The CEPSA periodically enhances the diversity of the population in the iterative process of the algorithm, which improves the exploration ability of the algorithm and prevents it from premature convergence. Based on the CEPSA, this paper proposes a new variant of the differential evolution algorithm, which is known as CEDE. In the experiment, the performance of CEDE was verified via the CEC 2014 and CEC 2017 benchmark test sets and several real-world engineering problems. CEDE was compared with 11 variants of differential evolution and six metaheuristic algorithms. The experimental results show that CEDE was significantly better than the compared algorithms. In addition, we conducted a sensitivity analysis on the parameters of CEDE, and the experimental results show that CEDE was not sensitive to the parameters, indicating that CEDE can be easily applied to various optimization problems.

关键词： Differential evolution Cosine-exponential Population size adaptive evolution algorithm

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A two stage differential evolution algorithm for parameter estimation of proton exchange membrane fuel cell

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SCIENTIFIC REPORTS 2025年第1期15卷 1-29页

作者： Aljaidi, Mohammad Agrawal, Sunilkumar P. Jangir, Pradeep Pandya, Sundaram B. Parmar, Anil Arpita Alkoradees, Ali Fayez Trivedi, Bhargavi Indrajit Khishe, Mohammad Zarqa Univ Fac Informat Technol Dept Comp Sci Zarqa 13110 Jordan Govt Engn Coll Dept Met Engn Gandhinagar 382028 Gujarat India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Graph Era Hill Univ Dept CSE Dehra Dun 248002 India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India Appl Sci Private Univ Appl Sci Res Ctr Amman 11937 Jordan Shri KJ Polytech Dept Elect Engn Bharuch India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia Vishwakarma Govt Engn Coll Ahmadabad Gujarat India Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan City Taiwan Jadara Univ Res Ctr Irbid Jordan

Precise models predicting fuel cell performance under different operating conditions require accurate parameter identification in a proton exchange membrane fuel cell (PEMFC). Most traditional parameter estimation methodologies depend on optimization algorithms which are limited in their efficiency, convergence speed, and robustness. Typically, existing algorithms fail to achieve a balance between precision and computational efficiency, leading to suboptimal modeling of the complex, nonlinear behavior of PEMFCs. In this paper, we present the two-stage differential evolution (TDE) algorithm, which fills these gaps by using a new mutation strategy that improves solution diversity and speeds up convergence. Seven critical unknown parameters (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\xi }_{1},{\xi }_{2},{\xi }_{3},{\xi }_{4},\beta ,{R}_{C},$$\end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}) in PEMFC models are identified by using the proposed TDE algorithm. The optimization process is to minimize the sum of squared errors (SSE) between the experimentally measured and predicted cell voltages. TDE resulted in a 41% reduction in SSE (minimum SSE of 0.0255 compared to 0.0432), a 92% improvement in maximum SSE, and over 99.97% reduction in standard deviation compared to the HARD-DE algorithm. Furthermore, TDE was shown to be 98% more efficient than HARD-DE, with a runtime of 0.23 s, compared to HARD-DE's runtime of 11.95 s. Extensive testing of these advancements was performed on six commercially available PEMFC stacks over twelve case studies, and I/V and P/V characte

关键词： Parameter estimation Proton exchange membrane fuel cell (PEMFC) Differential evolution evolution algorithm Optimization

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Mathematical formulation and a hybrid evolution algorithm for solving an extended row facility layout problem of a dynamic manufacturing system

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2022年 78卷

作者： Guan, Chao Zhang, Zeqiang Zhu, Lixia Liu, Silu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China Wenzhou Univ Sch Mech & Elect Engn Wenzhou 325035 Peoples R China

In this study, a dynamic extended row facility layout problem is investigated where the dynamic manufacturing process is considered. This problem involves the rearrangement cost when the location of the department changes over adjacent periods and it is aimed at minimising the sum of total material handling and rearrangement costs. The specific number of rows in each period is not predetermined in this study. Based on the product processing in practice, the essential clearance between departments and floor boundaries is also considered when calculating relative positions and exact locations of departments. A mixed-integer linear programming model is established for this problem. A hybrid evolution algorithm (HEA) that combines the exhaustive pairwise swap and variation operations is proposed to resolve this problem. First, a decoding strategy is designed to represent the department sequence and the optimal exact locations are solved using the linear programming method. Second, an exhaustive pairwise swap operation is addressed to search for the neighbourhood space without repeated calculations. This operation terminates when all swap pairs run out or preferable solution is found. Therefore, the variation operation starts immediately to switch to a new neighbourhood space. The HEA is tested with many instances and its good performance has been proven by comparing its results with existing solutions of fixed-row, flexible-row facility layout problems, and results of exact approach (CPLEX).

关键词： Manufacturing system design Dynamic layout problem Row facility layout problem Mixed-integer linear programming evolution algorithm

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evolution algorithm of parametric active contour model based on Gaussian smoothing filter

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MACHINE VISION AND APPLICATIONS 2022年第6期33卷 83-83页

作者： Tang, Kelun Zhou, Xiaojun Xihua Univ Chengdu 610039 Peoples R China

At present, the parametric active contour model is one of the most well-known and widely used image segmentation techniques in image processing and computer vision. However, its evolution computation is slow, which is a great obstacle to some applications such as real-time motion tracking. This paper not only reveals its bottleneck including the high computation cost of the inverse operation of matrix and the matrix multiplication in each iteration, but also proposes a novel scheme that transfers these time-consuming matrix operations into vector convolution operations for better performance. As shown by simulation results the proposed algorithm is always much faster than the conventional algorithm, and the velocity gain increases with the snaxels on the curve, from several times to over 2 orders of magnitude.

关键词： Gaussian smoothing filter evolution algorithm Parametric active contour model Convolution operation

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Multi-Objective Optimization Based on Adaptive evolution algorithm for Disassembly Line Network Problems 24

Multi-Objective Optimization Based on Adaptive Evolution Alg...

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24th International Conference on Advanced Communication Technology (ICACT) - Artificial Intelligence Technologies toward Cybersecurity

作者： Jian, Ming-Shen Wu, Wei-Sin Lin, Yi-Hsuan Wu, Xin-Mei Wang, Peng-Wen Natl Formosa Univ Dept CSIE Cloud Comp & Intelligent Serv Lab Huwei Taiwan

ISBN: (纸本)9791188428090

In recent years, the demand for disassembly is increasing. the variability of the feed product and the degree of damage that occurs during the disassembly process. In this research, the optimized mathematical programming models are proposed based on the transformed AND / OR graphic(TAOG) as a priority relation between jobs. To find the optimal solution, different steps and types of the evolution algorithm can be established via individual thread procedures and various virtual machines in cloud. The proposed improvement phase based on the fitness evaluation result according to different crossover methods can enhance the performance of the convergence.

关键词： Multi-Objective evolution algorithm disassembly line optimal solution

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A novel traffic flow prediction model: Variable order fractional grey model based on an improved grey evolution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期224卷

作者： Zhang, Ruyue Mao, Shuhua Kang, Yuxiao Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China

Short-time traffic flow prediction plays an essential role in Intelligent Transportation System. Mixed flow is a common type of short-time traffic flow, with characteristics of small sample size, high temporal variability, and evident phase. To better predict the changes of mixed flow and speed, a new variable order fractional grey model (NVOFGM) is proposed by introducing the variable order fractional integro-differential (VO-FID) model into the grey model and extending the first accumulation generation to variable order fractional accumulation generation (VO-FAGO). The analytical expression of NVOFGM is obtained using Laplace transform. At the same time, considering that NVOFGM needs to optimize the parameters, and the objective function of the optimization is the nonlinear function with many parameters, a new grey prediction evolution algorithm (CDEGPEA) is obtained by improving the grey prediction evolution algorithm by the chaotic logistic map and the differential evolution algorithm (DE), and the superiority and effectiveness of its performance are validated on CEC2005 benchmark functions. Finally, based on the mixed flow and speed data from the M3 and M25 freeways in the UK, it is found that NVOFGM performs well in both fitting and testing and has good stability and stable generalization ability.

关键词： Short -time traffic flow Grey model evolution algorithm Optimization

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Deep Reinforcement Learning evolution algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

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FUTURE INTERNET 2023年第1期15卷 34-34页

作者： Yang, Siyuan Bouazizi, Mondher Ohtsuki, Tomoaki Shibata, Yohei Takabatake, Wataru Hoshino, Kenji Nagate, Atsushi Keio Univ Grad Sch Sci & Technol Yokohama 2238522 Japan Keio Univ Fac Sci & Technol Dept Informat & Comp Sci Yokohama 2238522 Japan SoftBank Corp Technol Res Lab Tokyo 1350064 Japan

In this paper, we propose a novel Deep Reinforcement Learning evolution algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users' throughput. Different from other model-based reinforcement learning methods, such as the Deep Q Network (DQN), the proposed method combines the evolution algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

关键词： HAPS antenna control reinforcement learning evolution algorithm

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Improved evolution algorithm that Guides the Direction of Individual Mutation for Influence Maximization in Social Networks 14th

Improved Evolution Algorithm that Guides the Direction of In...

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14th International Conference on Knowledge Science, Engineering, and Management (KSEM)

作者： Tang, Xiaoqin Liu, Xuxing Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (纸本)9783030821531;9783030821524

The purpose of influence maximization in social networks is to find K nodes as the spread source to activate as many nodes as possible. To improve the efficiency and effectiveness of the classic genetic algorithm in large social networks, the diffusion evaluation function is first proposed to estimate the impact range of seed nodes. Then the individuals are initialized based on the diffusion degree centrality of the node. Adapted a crossover strategy is used to help the evolution algorithm to achieve the purpose of local search. Besides, a direction vector is designed to guide the individual's mutation. Through experiments on real social networks, the improved evolution algorithm can approximate the state-of-the-art greedy algorithm in the final result while also significantly improving time efficiency.

关键词： Influence maximization evolution algorithm Diffusion evaluation function Crossover strategy Direction vector

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An application of augmented Lagrangian differential evolution algorithm for optimizing the speed of inland ships sailing on the Yangtze River

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INTERNATIONAL JOURNAL OF NAVAL ARCHITECTURE AND OCEAN ENGINEERING 2022年 14卷

作者： Zhang, Longhui Peng, Xiuyan Liu, Zhengfeng Wei, Naxin Wang, Fei Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China China Ship Sci Res Ctr Wuxi Jiangsu Peoples R China Univ Sci & Technol China Hefei Natl Lab Phys Sci Microscale Sch Life Sci Hefei Anhui Peoples R China

As an effective way to achieve energy conservation and emission reduction, the speed optimization of inland ships has received increasing attention. In this study, a modeling method for the speed optimization of inland ships sailing on the Yangtze River and a heuristic algorithm called Augmented Lagrangian Differential evolution (ALDE) method are proposed. The ALDE introduces adaptive control parameters, an elite strategy, and opposition-based learning technology for optimization. To validate the performance of ALDE, it is compared with six other algorithms to solve several well-known constrained optimization problems, and the results show that the ALDE outperforms these six algorithms. To construct a speed optimization model, a discretization model for the Yangtze River waterway is established by segmenting the waterway. Considering the relationship among the environment, ship, engine, and propeller, a fuel consumption prediction model is built, and then, the objective optimization function for inland ship speed optimization is established by integrating the discrete model as well as the fuel consumption model. Finally, numerical simulation results show that the ALDE can realize less fuel consumption for an inland ship sailing from Yibin Port to Jiangyin Port, verifying the feasibility of the ALDE for speed optimization of inland ships. (c) 2022 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Inland ships Fuel consumption Speed optimization Augmented Lagrangian differential evolution algorithm Energy conservation

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