检索结果-内蒙古大学图书馆

Dual-clustering-based Two-population Co-evolutionary Algorithm for segmentation coding in flash memory

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 147卷

作者： Luo, Jianjun Chen, Menghao Huang, Boming Liu, Hailuan Fan, Lingyan Gao, Lijuan Feng, Guorui Hangzhou Dianzi Univ Hangzhou 310018 Zhejiang Peoples R China Hangzhou Hualan Microelect CO Ltd Hangzhou 310018 Zhejiang Peoples R China China Elect Technol Grp Corp Res Inst 52 Hangzhou 310018 Zhejiang Peoples R China Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China

To ensure the reliability of stored data, error checking and correction (ECC) module is widely used in flash memory-based storage devices. With the increase of raw bit error rate (RBER), the traditional error correction coding mode is not only hard to satisfy the requirements of storage devices, but also reduces the efficiency of data recovery when error data cannot be located. In this paper, a novel segmentation coding mode is proposed to improve the drawbacks of traditional coding mode. To our knowledge, this is an innovative study of the error correction coding mode in ECC. It can make the coding mode used in ECC to become more flexible and efficient. Since the performance of this coding mode is related to segmentation, we propose a dual-population co-evolutionary algorithm based on clustering algorithm to optimize the performance. The proposed algorithm adopts clustering algorithm to measure the diversity of the population and dynamic weight allocation strategy to regulate evolution indicators of the population. Some experiments are conducted on benchmark problems and the segmentation coding problem, respectively. Experimental results show that the proposed algorithm is superior to other state-of-the-art evolutionary algorithms.

关键词： Flash memory Error checking and correction Bose-Chaudhuri-Hocquenghem code constrained multiobjective optimization Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

multiobjective Synthesis of Ultra-Wideband Array Antennas in the Time Domain With High-Power Pulse

引用

Microwave and Optical Technology Letters 2025年第5期67卷

作者： Xinyu Jiang Youjie Yan Kaiyue Zhang Chengyun Cao Liangjie Bi Lin Meng School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China Northwest Institute of Nuclear Technology Xian China

Ultra-wideband (UWB) is a typical high-power electromagnetic (HPEM) interference environment. The important parameters that affect the performance of UWB HPEM simulators include electric field strength ( E p p ${E}_{pp}$ ), waveform consistency ( t p p v a r ${t}_{ppvar}$ ), and test space (half-power beamwidth (HPBW)). In this letter, the constrained multiobjective optimization algorithm NSGA-II is used to optimize the time delay of the array element based on the principle of convolution of time domain array patterns. The appropriate solution on the Pareto front according to the actual need is used to realize a stable radiation field with a certain amplitude over a larger test space. Under one of the sets of delays, the E p p ${E}_{pp}$ of this array is reduced by no more than 70% and the HPBW is increased from 16.1° to 24°. The rate of change of pulse width is reduced from 21.5% to 5% corresponds to a more stable spectral. Each element of the array is an antipodal Vivaldi antenna. A 9-element sample of Pareto fronts is simulated by using CST Microwave Studio software. The synthesis results demonstrate the efficiency and accuracy of the method and quickly provide valuable information for array antenna designers.

关键词： constrained multiobjective optimization high-power electromagnetic (HPEM) time domain UWB pulse antenna array

来源：评论

学校读者我要写书评

暂无评论

ε-constrained multiobjective differential evolution using linear population size expansion

引用

INFORMATION SCIENCES 2022年 609卷 445-464页

作者： Ji, Jing-Yu Zeng, Sanyou Wong, Man Leung Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

constrained multiobjective optimization problems commonly arise in real-world applications. In the presence of constraints and multiple conflicting objectives, finding a set of feasible solutions which can best tradeoff different objectives is quite challenging. In this study, an e-constrained multiobjective differential evolution using linear population size expansion is proposed to solve such a kind of problems. First, the e-constraint -handling method, which originally solves constrained optimization problems with only one objective, is further improved to handle constraints in a multiobjective optimization way. Second, to achieve a better approximation to the feasible Pareto front, a linear popu-lation size expansion strategy is developed. Once enough feasible solutions have been found, the population size will be linearly increased to find more promising solutions. As a result, a simple yet efficient constrained multiobjective differential evolution is proposed. Experiments are conducted to evaluate the performance of the proposed algorithm on 35 benchmark test functions with different numbers of constraints and objectives. Obtained results are compared with seven state-of-the-art algorithms. Empirical results and compar-isons demonstrate that our proposed algorithm achieves better or at least comparable per-formance to the competitors, and is capable of obtaining a set of representative feasible solutions for the selected real-world constrained multiobjective optimization problems, especially for highly constrained problems.(c) 2022 Elsevier Inc. All rights reserved.

关键词： constrained multiobjective optimization e-constrained-handling method Linear population size expansion Differential evolution Real-world engineering applications

来源：评论

学校读者我要写书评

暂无评论

Dynamic Selection Preference-Assisted constrained multiobjective Differential Evolution

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 2954-2965页

作者： Yu, Kunjie Liang, Jing Qu, Boyang Luo, Yong Yue, Caitong Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China

Solving constrained multiobjective optimization problems brings great challenges to an evolutionary algorithm, since it simultaneously requires the optimization among several conflicting objective functions and the satisfaction of various constraints. Hence, how to adjust the tradeoff between objective functions and constraints is crucial. In this article, we propose a dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm. In our approach, the selection preference of each individual is suitably switching from the objective functions to constraints as the evolutionary process. To be specific, the information of objective function, without considering any constraints, is extracted based on Pareto dominance to maintain the convergence and diversity by exploring the feasible and infeasible regions;while the information of constraint is used based on constrained dominance principle to promote the feasibility. Then, the tradeoff in these two kinds of information is adjusted dynamically, by emphasizing the utilization of objective functions at the early stage and focusing on constraints at the latter stage. Furthermore, to generate the promising offspring, two DE operators with distinct characteristics are selected as components of the search algorithm. Experiments on four test suites including 56 benchmark problems indicate that the proposed method exhibits superior or at least competitive performance, in comparison with other well-established methods.

关键词： optimization Linear programming Statistics Sociology Convergence Diversity methods Switches constrained multiobjective optimization constraint handling constraints differential evolution (DE) dynamic selection preference objective functions

来源：评论

学校读者我要写书评

暂无评论

Feature Extraction for Recommendation of constrained multiobjective Evolutionary Algorithms

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2023年第4期27卷 949-963页

作者： Qiao, Kangjia Yu, Kunjie Qu, Boyang Liang, Jing Yue, Caitong Ban, Xuanxuan Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China

The evolutionary algorithm recommendation is catching increasing attention when solving practical application problems since different algorithms often perform differently on different problems. To achieve the algorithm recommendation, extracting effective features to accurately characterize the problems is necessary, which is related to the feature extraction problem. So far, most feature extraction methods focus on single-objective optimization problems, and only a few studies are conducted on multiobjective optimization problems and constrained optimization problems, let alone constrained multiobjective optimization problems (CMOPs) that are widely encountered in the real world. To fill the gap, this article proposes an evolution-based constrained multiobjective feature extraction method (ECMOFE), in which the information generated in the evolutionary process is leveraged to form the feature matrix. To be specific, we create two populations to, respectively, optimize constraints and objectives for some generations. Furthermore, two complementary evolutionary operators are used to generate offspring for each population. In the environmental selection, the successful rate of offspring individuals generated by each operator of each population is recorded to form the feature matrix. Then, a dimension reduction method is designed to compress the size of the feature matrix. By the above process, the feature vector that can reflect the global relationship between constraints and objectives and the difficulty of the CMOP is formed. Based on the formed features, several algorithm recommendation methods are built on the basis of classifiers. The results based on multiple metrics show the effectiveness of the proposed ECMOFE.

关键词： Algorithm recommendation constrained multiobjective optimization feature extraction

来源：评论

学校读者我要写书评

暂无评论

A Cooperative Multistep Mutation Strategy for multiobjective optimization Problems With Deceptive Constraints

引用

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2024年第11期54卷 6670-6682页

作者： Qiao, Kangjia Yu, Kunjie Yue, Caitong Qu, Boyang Liu, Mengnan Liang, Jing East China Univ Sci & Technol Control Sci & Engn Shanghai Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Zhengzhou Peoples R China State Key Lab Intelligent Agr Power Equipment Luoyang Peoples R China Chinese Soc Agr Machinery Beijing Peoples R China Henan Inst Technol Sch Elect Engn & Automat Xinxiang Henan Peoples R China

constrained multiobjective optimization problems with deceptive constraints (DCMOPs) are a kind of complex optimization problems and have received some attention. For DCMOPs, the closer a solution is to the feasible region, the larger its constraint value. Moreover, multiple local infeasible regions will have different minimal constraint values according to their distances to feasible regions. Therefore, most of the existing algorithms are easy to fall into local regions, and even cannot find any feasible solution. To address DCMOPs, this article proposes a new evolutionary multitasking algorithm with a cooperative multistep mutation strategy. In this algorithm, the DCMOP is transformed into a multitasking optimization problem, in which the main task is the original DCMOP and the created auxiliary task aims to provide effective help for solving the main task. Specially, the designed cooperative multistep mutation strategy contains two contributions to solve deceptive constraints. First, a multistep mechanism is proposed, in which the individuals will use multiple different steps to generate the multiple offspring solutions along one direction, so as to expand search range to find feasible regions. Second, a cooperative mechanism between the two tasks is proposed, in which the main purpose is to provide effective and stable search directions. To be specific, an opposite solution generation method is utilized to generate the opposite solution of auxiliary population in the search space, and the direction from the auxiliary population to the main population will be formed. Combined with these two mechanisms, the proposed cooperative multistep mutation strategy can effectively improve the population diversity along the promising and stable search directions. In the experiments, the proposed algorithm is tested on the two benchmark DCMOPs, which contain objective space constraints and decision space constraints respectively. The results show the effectiveness and superio

关键词： optimization Task analysis Shape Search problems Multitasking Convergence Space exploration constrained multiobjective optimization cooperative deceptive constraints evolutionary multitasking (EMT) multistep mutation strategy opposite solution generation method

来源：评论

学校读者我要写书评

暂无评论

Joint Interdependent Task Scheduling and Energy Balancing for Multi-UAV-Enabled Aerial Edge Computing: A multiobjective optimization Approach

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第23期10卷 20368-20382页

作者： Huang, Xumin Peng, Chaoda Wu, Yuan Kang, Jiawen Zhong, Weifeng Kim, Dong In Qi, Long Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Univ Macau State Key Lab Internet Things Smart City Macau Peoples R China South China Agr Univ Coll Math & Informat Guangzhou 510642 Peoples R China Univ Macau Dept Comp & Informat Sci Macau Peoples R China Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China

To provide a dependency-aware application, multiple unmanned aerial vehicles (UAVs) are employed to serve a ground user with a set of interdependent tasks. This leads to a new computing paradigm called as multi-UAV-enabled aerial edge computing (MU-AEC). For the large-scale application of MU-AEC, both the task-centric objective and UAV-centric objective should be simultaneously considered. Thus, we focus on the joint interdependent task scheduling and energy balancing for MU-AEC by using a multiobjective optimization approach, which enables a decision maker to identify the optimal solutions corresponding to the best feasible tradeoffs between the two objectives. A constrained multiobjective optimization problem involving two objectives: 1) the makespan minimization of all tasks and 2) energy balancing among different UAVs, is formulated. In the solution methodology, we propose a constrained decomposition-based multiobjective evolution algorithm. To quickly seek more superior solutions, a local search mechanism by utilizing the objective information, and an improved genetic operator are proposed for remarkable performance improvements. Finally, numerical results demonstrate that compared with the baseline algorithms, our algorithm achieves both advantages in increasing the convergence and diversity of the solutions.

关键词： Task analysis optimization Autonomous aerial vehicles Energy consumption Resource management Edge computing Trajectory constrained multiobjective optimization energy balancing evolutionary algorithm interdependent task scheduling unmanned aerial vehicle (UAV)

来源：评论

学校读者我要写书评

暂无评论

constrained multiobjective Decomposition Evolutionary Algorithm for UAV-Assisted Mobile Edge Computing Networks

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第22期11卷 36673-36687页

作者： Zhang, Lei Wen, Fangqing Zhang, Qinghe Gui, Guan Sari, Hikmet Adachi, Fumiyuki China Three Gorges Univ Hubei Key Lab Intelligent Vis Based Monitoring Hyd Yichang 443002 Peoples R China Hubei Univ Automot Technol Inst Vehicle Informat Control & Network Technol Shiyan 442002 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443005 Peoples R China Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Tohoku Univ Int Res Inst Disaster Sci Sendai 9800845 Japan

The increasing significance of unmanned aerial vehicles (UAVs) in mobile edge computing (MEC) has captured considerable attention. Nevertheless, the effectiveness of UAVs-assisted MEC networks is hampered by challenges, such as limited communication capacity and onboard power. To tackle these issues, this study develops a constrained multiobjective optimization model designed to enhance the performance of UAVs-assisted MEC networks, focusing on system capacity, energy consumption, and task latency. As a result, this problem manifests as a complex constrained multiobjective optimization problem. The study then proposes a constrained multiobjective decomposition evolutionary algorithm (CMODEA) with low-computational complexity. This algorithm employs an adaptive individual comparison strategy, balancing diversity and convergence, and integrates an optimally guided differential evolution strategy for efficiently approximating optimal solutions. Additionally, it incorporates an adaptive constraint handling method, effectively managing existing constraints. The CMODEA aims to simultaneously optimize system capacity, energy consumption, and task latency while meeting the computational resource requirements of UAVs and ensuring acceptable user task latency levels. Simulation results demonstrate the algorithm's effectiveness in significantly enhancing capacity, reducing energy consumption and latency, without greatly increasing algorithm complexity.

关键词： Task analysis optimization Energy consumption Internet of Things Autonomous aerial vehicles Convergence Delays 6G constrained multiobjective optimization evolutionary computation mobile edge computing (MEC) unmanned aerial vehicles (UAVs)

来源：评论

学校读者我要写书评

暂无评论

Experimental Case Study of Stochastic Surrogate-Assisted Engine Calibration

引用

IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第6期27卷 4897-4907页

作者： Pal, Anuj Zhu, Ling Wang, Yan Zhu, Guoming G. Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Ford Motor Co Dearborn MI 48124 USA

Reducing experimental calibration cost to achieve optimal system performance is challenging, especially for highly nonlinear engine systems. With the advancement and widespread adoption of machine learning methods for control applications, it is now possible to use an intelligent black-box model to efficiently optimize nonlinear control systems without the detailed knowledge of system dynamics. The surrogate-assisted optimization approach has been recently proven to work effectively to reduce the overall experimental budget (cost). However, its application to calibrate and optimize a practical control system experimentally is a challenge due to stochastic system and measurement noises. This article experimentally calibrates and optimizes the control parameters of a 6.7L Ford diesel engine using the stochastic surrogate-assisted optimization automatically based on a stochastic Kriging model, which is the first step toward automating the engine calibration process. Two engine control variables, namely, exhaust gas recirculation valve and variable geometry turbocharger vane positions, are calibrated to obtain a tradeoff between engine efficiency, in terms of brake-specific fuel consumption, and NOx emissions, utilizing both the deterministic and stochastic surrogate-assisted learning algorithms. Experimental results show a much better representation of the output response surfaces with the stochastic surrogate-assisted optimization than that from the deterministic approach. Note that the stochastic approach helps direct the search toward the global optimal region without wasting the evaluation budget in exploring the local optima, and the deterministic approach cannot effectively handle stochastic system and measurement noises, resulting in overfitting the data. Therefore, to solve a practical optimization problem with noises, stochastic surrogate-assisted optimization is recommended.

关键词： constrained multiobjective optimization engine dyno test stochastic optimization surrogate model

来源：评论

学校读者我要写书评

暂无评论

A constrained multiobjective evolutionary algorithm with the two-archive weak cooperation

引用

INFORMATION SCIENCES 2022年 615卷 415-430页

作者： Li, Yingwei Feng, Xiang Yu, Huiqun East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China Shanghai Engn Res Ctr Smart Energy Shanghai 200237 Peoples R China

The significant issue to solve constrained multiobjective optimization problems (CMOPs) is to keep the balance between objectives and constraints, but the existing evolutionary algorithms are in the face of challenges for solving CMOPs with complex feasible regions. Based on this purpose, a constrained multiobjective evolutionary algorithm with the two-archive weak cooperation (CMOEA-TWC) is proposed in this article. In CMOEA-TWC, two archives are evolved, which are denoted as driving archive and normal archive, respectively. Besides, the weak cooperation of two archives is designed for sharing valuable information between archives. Specifically, the driving archive only considers objectives, while the normal archive considers both objectives and constraints. In addition, the minimum shiftbased density estimation-based (SDE) distance is adopted to enhance the diversity of solutions in the driving archive, and a strict constrained dominance principle is designed to improve the feasibility of solutions in the normal archive. The proposed algorithm is tested on 47 CMOPs of 4 benchmark suites with the comparison of 4 state-of-the-art algorithms. The experimental results demonstrate that the average ranks in terms of inverted generational distance (IGD) outperform those of existing state-of-the-art competitors.(c) 2022 Published by Elsevier Inc.

关键词： Two -archive weak cooperation constrained handling constrained multiobjective optimization Evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：