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Handling Multiobjective Optimization Problems With Complex Constraints: A Constraints Grouping-Based Approach

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年

作者： Xu, Biao Zheng, Yiwu Li, Wenji Gao, Xiaozhi Gong, Dunwei He, Jie Fan, Zhun Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou 543002 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Univ Eastern Finland Dept Comp Sci Joensuu 80101 Finland Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266100 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China

Real-world production scenarios often involve multiobjective optimization problems with intricate constraints. Although there has been a growing interest in multiobjective problems with complex constraints, such as the vehicle routing problem with time windows, existing multiobjective evolutionary optimization techniques still face significant challenges, particularly when addressing the fragmented and narrow feasible regions that arise from these constraints. Our research introduces a refined framework tailored for complex constrained multiobjective evolutionary optimization. The methodology conducts an initial strong-weak analysis to categorize constraints and merges each strong constraint with all weak constraints to form subsets. Each subset, combined with the original objective functions, defines a subproblem. Independent optimization of the original problem and subproblems is carried out by utilizing multiple populations. Information acquired from the subproblems' populations is transferred into the population of the original issue, thereby expediting the detection of the feasible region and simplifying the resolution of the original problem. The efficacy of our innovative algorithm, when benchmarked against traditional constrained multiobjective evolutionary algorithms across 72 test functions, has demonstrated superior convergence, diversity, and competitiveness.

关键词： Optimization Linear programming Search problems Production partitioning algorithms Pareto optimization Intelligent control Fans Convergence Vectors Co-evolutionary complex constraints constraints grouping multiobjective optimization

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New Evaluation Method for Fuzzy Cluster Validity Indices

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IEEE ACCESS 2025年 13卷 22728-22744页

作者： Perez-Sanchez, Ismay Medina-Perez, Miguel Angel Monroy, Raul Loyola-Gonzalez, Octavio Gutierrez-Rodriguez, Andres Eduardo Tecnol Monterrey Sch Engn & Sci Atizapan De Zaragoza 52926 Estado De Mexic Mexico Kayak Analyt Dubai U Arab Emirates NTT DATA Madrid 28050 Spain Tecnol Monterrey Inst Future Educ Monterrey 64849 Mexico MAHLE Shared Serv Monterrey 64650 Mexico

Cluster analysis is the process of associating data objects with classes of similar objects. One key aspect of the clustering problem is to determine the optimal number of groups on a data set to effectively partition it. The process of establishing accurate indicators is called cluster validity, and it evaluates the quality of a clustering, including the optimal number of clusters. A cluster validity index (CVI) is a function that carries out that evaluation based on a series of features like intra-class compactness, inter-class separation, object density, and membership degree of objects to each cluster, among others. Best-c validation protocol has been used to evaluate CVIs previously, but it makes a strong assumption about the correctness of the clustering performed. We propose a new protocol based on mapping the internal CVI values to an external CVI and performing statistical tests to analyze the results. The experiments are conducted evaluating the partitions generated by the fuzzy c-means clustering algorithm over 84 UCI datasets with 31 fuzzy CVIs. Experimental results evidenced the need for bigger experimental setups compared to previous studies. Also, they show that among the CVIs involved, there is none with a significant statistical difference over the other given by a Friedman test. However, the Wilcoxon signed-rank test demonstrates that in 1 vs. 1, the CVI KPBM outperforms the others in most of the fuzzifiers (m). Just except for m = 2.0 which consolidates the results in previous studies where none of the CVIs were able to improve consistently among the selected group of CVIs.

关键词： Clustering algorithms partitioning algorithms Indexes Protocols Prediction algorithms Hands Weather forecasting Technological innovation Organizations Object recognition Fuzzy cluster validation index evaluation method fuzzy clustering fuzzy sets

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High-Precision Deblending of 3-D Simultaneous Source Data Based on Prior Information Constraint

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Zhang, Hua Bao, Xingyue Zhao, Hu Hao, Yaju Huang, Hongwei Dai, Mengxue Liu, Weihua East China Univ Technol Natl Key Lab Uranium Resources Explorat Min & Nucl Nanchang 330013 Peoples R China SINOPEC Key Lab Geophys Nanjing 211103 Peoples R China Southwest Petr Univ Sch Geosci & Technol Chengdu 610500 Peoples R China

How to effectively remove blending noise is the key to the success of simultaneous source acquisition. The traditional deblending of 3-D simultaneous source data is based on the energy difference between blending noise and useful signals in the sparse domain and removes blending noise independently of each 2-D partition of 3-D simultaneous source data using a sparse inversion algorithm. However, these 3-D deblending techniques do not fully utilize a priori information derived from the locations of the spare coefficients from different partitions, especially when highly redundant curvelet domain sparsity is used. In this letter, we propose to use the priori support information to improve the deblending performance of 3-D simultaneous source data by using a 2-D curvelet transform. First, based on the correlations between the support sets of the significant curvelet coefficients of adjacent partitions, a weighted operator is constructed in the curvelet domain using the prior support sets provided by the previous partition that has removed blending noise. Then, incorporate it into the iterative shrinkage thresholding algorithm (ISTA) to delineate the main energy distribution range of the useful signals, and define a sequence of 2-D curvelet-based deblending problems that exploit 3-D correlations exhibited by seismic wavefields without using the highly redundant 3-D curvelet transform. To test the performance of our proposed method, we compared deblending from synthetic and field artificially blended data. These examples demonstrated that our method is superior to the traditional iterative algorithm in terms of deblending performance, and computational memory requirements.

关键词： Noise Three-dimensional displays Geoscience and remote sensing partitioning algorithms Accuracy Iterative algorithms Frequency-domain analysis Correlation Uranium Transforms Deblending iterative shrinkage thresholding algorithm (ISTA) prior information weighted operator

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Partitioned Scheduling With Shared Resources on Imprecise Mixed-Criticality Multiprocessor Systems

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第1期44卷 65-76页

作者： Zhang, Yi-Wen Ma, Jin-Peng Gu, Zonghua Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Hofstra Univ Dept Comp Sci Hempstead NY 11549 USA Umea Univ Dept Appl Phys & Elect S-90187 Umea Sweden

Both resource access protocols and real-time scheduling algorithms have been extensively studied in classic embedded real-time systems. However, there has been relatively little attention given to the resource access protocol and real-time scheduling algorithms in mixed-criticality systems. In this article, we pay attention to the problem of scheduling an imprecise mixed-criticality (IMC) taskset on a multiprocessor platform with shared resources. First, we propose an IMC with MSRP (IMC-MSRP) resource access protocol, which ensures mutually exclusive access to the shared resources for the tasks. Second, we propose the schedulability test based on the IMC-multiprocessor stack resource policy (MSRP) for a given task-to-processor mapping method. Third, we propose a feasible task-to-processor mapping algorithm called resource-aware criticality-unaware worst-fit decreasing (RA-CU-WFD), which first assigns tasks sharing the same resources to the same processor to reduce the global waiting time of the tasks and thus improve the schedulability ratio of the system. And then assigns tasks based on the criticality-unaware worst-fit decreasing (CU-WFD) algorithm. Finally, we conduct experiments using the synthetic tasksets, and the experimental results show that the RA-CU-WFD outperforms the other approaches in terms of the schedulability ratio.

关键词： Task analysis Program processors Job shop scheduling Access protocols Scheduling algorithms Quality of service partitioning algorithms Imprecise mixed-criticality (IMC) multicore partitioned scheduling shared resource

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Fast Domain Decomposition Algorithm Using Barycentric Interpolation and Overlapping Subdomains for 3D Multiscale Problems

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2025年 10卷 198-208页

作者： Kielian, Nils Stiemer, Marcus Univ Fed Armed Forces Hamburg Helmut Schmidt Univ Dept Theory Elect Engn & Numer Field Computat D-22043 Hamburg Germany

A robust data-transfer process for balanced domain decomposition is presented. This algorithm reduces the number of mesh elements required to solve certain 3D multiscale problems with the finite element method. In some examples, a reduction factor of up to 5 has been observed. The reduction is achieved by introducing an overlapping auxiliary domain to an originally non-overlapping domain decomposition scheme, allowing for individual meshing of the subdomains. The data transfer to couple the individually meshed subdomains is performed with the help of barycentric interpolation. Hence, the advantages of parallel solution of subdomain problems is combined with a stable inter-subdomain data transfer. The developed algorithm can be applied on problems with a scalar valued second order spatial elliptic differential operator in various fields of engineering, such as semiconductors, huge and complex biological cell clusters, heat conducting and pressure problems on multiple scales.

关键词： Data transfer Couplings Finite element analysis Interpolation Mortar Convergence Differential operators Boundary conditions partitioning algorithms Iterative methods Domain decomposition finite element method interface problems electro-quasistatic problems

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Acceleration of an Optimized Kvazaar All Intra Prediction on Embedded Systems Based on the Directional Texture Complexity

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IEEE EMBEDDED SYSTEMS LETTERS 2025年第1期17卷 38-41页

作者： Majok, James R. Abo-Zahhad, Mohammed Inoue, Koji Sayed, Mohammed S. Egypt Japan Univ Sci & Technol Dept Elect & Commun Engn Alexandria 21934 Egypt Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan

The high growth of real-time video applications on embedded systems poses challenges for practical encoders aiming to deliver high quality and high speed simultaneously. In real-world video applications, the slower preset in Kvazaar HEVC encoder can achieve impressive quality, with a penalty of extensive computational time. This is ultimately due to rate-distortion optimization that involves a comprehensive analysis of all possible quad-tree partitioning within the coding tree unit (CTU) structure, resulting in unpleasant encoding complexity. This letter proposes a method of accelerating All Intraprediction on Nvidia Jetson TX1 using early termination of CTU partitioning and a method of selecting only eight modes for intraframe search. The proposed technique reduces the running time of an optimized Kvazaar all intraprediction by 48.4% and 40.24% at slower and higher presets, respectively, with an average BD rate lost of 1.5% and 0.682% compared to the optimized Kvazaar running under the same coding configuration.

关键词： Encoding Complexity theory partitioning algorithms Embedded systems Visualization Streaming media Standards All intra coding tree unit (CTU) partition Kvazaar HEVC encoder rough mode decision (RMD) texture-based complexity

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A Hardware-Friendly Lightweight Partition Decision Algorithm for VVC Intra and Inter Coding

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 941-945页

作者： Zan, Zhao Huang, Leilei Chen, Shushi Zeng, Xiaoyang Fan, Yibo Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 201203 Peoples R China East China Normal Univ Inst Microelect Circuits & Syst Shanghai 200241 Peoples R China

The Versatile Video Coding (VVC) standard notably enhances encoding efficiency with the Quad-Tree plus Multi-Type Tree (QTMTT) partition structure. However, the complex QTMTT tool presents substantial challenges in both software and hardware implementation. To overcome those challenges, this paper introduces a hardware-friendly partition decision algorithm for VVC intra and inter coding. Firstly, we propose a lightweight backbone network to extract partition-aware features. Secondly, we employ a Quantisation Parameter (QP) fusion network to regulate the impact of QPs on the partition structure. Additionally, we apply a top-down threshold-driven post-processing algorithm, in which improbable partition types are removed to directly derive the unique partition structure. Experiments show that our method not only exceeds the previous state-of-the-art work in BD-BR performance, but also shows sufficient hardware-friendly characteristics. To the best of our knowledge, this work is among the earliest to comprehensively discuss and implement a hardware-friendly partition decision algorithm.

关键词： partitioning algorithms Hardware Encoding Signal processing algorithms Prediction algorithms Feature extraction Complexity theory Software algorithms Convolution Software VVC QTMTT partition decision complexity reduction hardware-friendly algorithm convolutional neural network intra coding inter coding

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Exploring Evolutionary Spectral Clustering for Temporal-Smoothed Clustered Cell-Free Networking

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 494-498页

作者： Wang, Junyuan Wu, Tianyao Zhou, Ouyang Zhu, Yaping Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Sch Comp Sci & Technol Shanghai 201804 Peoples R China

Clustered cell-free networking, which dynamically partitions the whole network into nonoverlapping subnetworks, has been recently proposed to mitigate the cell-edge problem in cellular networks. However, prior works only focused on optimizing clustered cell-free networking in static scenarios with fixed users. This could lead to a large number of handovers in the practical dynamic environment with moving users, seriously hindering the implementation of clustered cell-free networking in practice. This letter considers user mobility and aims to simultaneously maximize the sum rate and minimize the number of handovers. By transforming the multi-objective optimization problem into a time-varying graph partitioning problem and exploring evolutionary spectral clustering, a temporal-smoothed clustered cell-free networking algorithm is proposed, which is shown to be effective in smoothing network partitions over time and reducing handovers while maintaining similar sum rate.

关键词： Handover Optimization Clustering algorithms Cellular networks partitioning algorithms Transforms Uplink Signal to noise ratio Precoding Heuristic algorithms Clustered cell-free networking user mobility temporal smoothness handover evolutionary spectral clustering

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-Graph: A Graph Embedding for Interpretable Time Series Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第5期37卷 2680-2694页

作者： Boniol, Paul Tiano, Donato Bonifati, Angela Palpanas, Themis Inria PSL CNRS ENS F-75005 Paris France Univ Modena I-42121 Modena Italy Lyon 1 Univ IUF LIRIS CNRS Villeurbanne F-69622 Villeurbanne France Univ Paris Cite IUF F-75006 Paris France

Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with centroids. In addressing this gap, our work presents k-Graph, an unsupervised method explicitly crafted to augment interpretability in time series clustering. Leveraging a graph representation of time series subsequences, k-Graph constructs multiple graph representations based on different subsequence lengths. This feature accommodates variable-length time series without requiring users to predetermine subsequence lengths. Our experimental results reveal that k-Graph outperforms current state-of-the-art time series clustering algorithms in accuracy, while providing users with meaningful explanations and interpretations of the clustering outcomes.

关键词： Time series analysis Feature extraction Clustering algorithms Accuracy Heuristic algorithms Clustering methods Training Shape partitioning algorithms Directed graphs Time series clustering interpretability

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An Effective Genetic Algorithm for Mixed Precision

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IEEE ACCESS 2025年 13卷 62739-62752页

作者： Zhang, Wanyu Shang, Yu Tsao, Min Li, Yiwei Song, Xiaoyu Portland State Univ Dept Elect & Comp Engn Portland OR 97207 USA Siemens EDA Fremont CA 94538 USA

The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic algorithm to determine the maximum number of variables that can sustain precision alterations without compromising the desired error threshold. The proposed approach is evaluated across various benchmark programs, analyzing the effects of precision tuning under increasing error thresholds. Our findings reveal that, for a majority of these programs, reducing precision through partitioning leads to significant performance enhancements, with improvements of up to 15%.

关键词： Tuning Taylor series Accuracy Error analysis partitioning algorithms Computational modeling Computational efficiency Approximation algorithms Heuristic algorithms Optimization Floating-point arithmetic algorithmic differentiation dynamic program analysis mixed precision genetic algorithm

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