Article
Computer Science, Artificial Intelligence
S. Mohan, Akash Sinha
Summary: This paper proposes a novel method for performing nondominated sorting in a multiobjective optimization problem using a modified directional Bat algorithm. Unlike NSGA-II, the proposed algorithm generates and compares new solutions with all previous solutions, reducing computational time and generating diverse solutions. A unique sorting method using a Nondomination matrix is introduced, which can be easily updated to include new solutions and preserve elitism. Detailed criteria are provided for the selection of new solutions. Experimental results show that the proposed algorithm is competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most benchmark optimization problems. The algorithm also provides a standardized platform for nondomination sorting, applicable to any other metaheuristic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Hanen Ben Ammar, Wafa Ben Yahia, Omar Ayadi, Faouzi Masmoudi
Summary: This paper addresses a multi-item capacitated lot-sizing problem with setup times and backlogging, proposing two new versions of multiobjective binary particle swarm optimization algorithms and conducting experimental comparisons to evaluate the efficiency.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Physics, Multidisciplinary
Huidong Ling, Xinmu Zhu, Tao Zhu, Mingxing Nie, Zhenghai Liu, Zhenyu Liu
Summary: This paper proposes a parallel multiobjective PSO weighted average clustering algorithm based on Apache Spark. The algorithm divides the entire dataset into multiple partitions and caches the data in memory using distributed parallel and memory-based computing of Apache Spark. The local fitness value of each particle is calculated in parallel according to the data in each partition, reducing the communication of data in the network. Additionally, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results.
Article
Engineering, Electrical & Electronic
Chen Xing, Kang Li, Li Zhang, Zhongbei Tian
Summary: Railway electrification is an important aspect of global transport decarbonization efforts. This article proposes a biobjective robust optimization model to minimize energy consumption and journey time in train operations, considering uncertain train mass associated with passenger load variations. A novel multiobjective optimization algorithm, p-nondominated sorting genetic algorithm-II (NSGA-II), is proposed to handle decision-makers' preferences and improve robustness. Numerical case studies confirm the effectiveness of the proposed algorithm, with up to 40.59% improvement in robustness compared to the original NSGA-II.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Engineering, Electrical & Electronic
Guifu Du, Chengqian Zhu, Xingxing Jiang, Qiaoyue Li, Weiguo Huang, Jie Shi, Zhongkui Zhu
Summary: This article discusses frequent power supply accidents and excessive energy consumption in dc traction power systems (dc TPSs) of multitrain subway lines. A multiobjective optimization approach is proposed to optimize the traction substation (TSS) converter characteristic and train timetable for safe and economic operation of subway systems. The results show significant reductions in the optimized objectives, namely rail potential (RP), energy consumption, and maximum traction current, indicating improved energy conservation and power supply safety in subway systems.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2023)
Article
Computer Science, Artificial Intelligence
Pengbo Wang, Houxiu Xiao, Xiaotao Han, Fan Yang, Liang Li
Summary: This paper proposes an archive-assisted evolutionary framework to address the challenges faced by evolutionary algorithms in constrained multiobjective optimization problems. The framework uses a reference line guided archive, an adaptive mating selection operator, and skipping infeasible regions for extensive searches to improve the feasibility, convergence, and diversity of the algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
Javier Moreno, Daniel Rodriguez, Antonio J. Nebro, Jose A. Lozano
Summary: This article introduces a new efficient algorithm MNDS for computing the nondominated sorting procedure, which outperforms other techniques in terms of computational complexity and running time. The algorithm is based on the computation of the dominance set and takes advantage of the characteristics of the merge sort algorithm.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Environmental Sciences
Swasti Dhagat, Satya Eswari Jujjavarapu
Summary: The study demonstrated the potential of Brevibacillus borstelensis to produce bioemulsifier and exopolysaccharide simultaneously, with their yield limited. By utilizing optimization techniques, the concentrations of both products were successfully increased. NSGA was found to be the most effective method for optimizing multiple responses simultaneously.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2021)
Article
Chemistry, Multidisciplinary
Lingren Kong, Jianzhong Wang, Peng Zhao
Summary: Dynamic weapon target assignment (DWTA) is an effective method for solving the multi-stage battlefield fire optimization problem, with a meaningful and effective model established in this paper. The model includes conflicting objectives of maximizing combat benefits and minimizing weapon costs, as well as various constraints. An improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed to solve the complex DWTA problem, showing better convergence and distribution compared to other state-of-the-art algorithms in experimental results.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Ieva Meidute-Kavaliauskiene, Nihal Sututemiz, Figen Yildirim, Shahryar Ghorbani, Renata Cincikaite
Summary: This paper focuses on cost optimization, scheduling trucks, and green supply chains in cross docking to reduce storage space, inventory management costs, and customer order delivery time. It aims to minimize total operating costs, truck transportation sequences, and carbon emissions in the supply chain. The non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are utilized to find near-optimal solutions to the problem, and the superior algorithm in each criterion is identified through comparison.
Article
Engineering, Multidisciplinary
Tung Tran The, Bao-Huy Truong, Khanh Dang Tuan, Dieu Vo Ngoc
Summary: This paper introduces a new multiobjective algorithm NSSFS for distributed network reconfiguration with distributed generation placements, which optimizes real power loss, voltage profile, and voltage stability index simultaneously, enhancing the system performance. The NSSFS algorithm shows better solution quality than other multiobjective techniques in dealing with the MODNR-DG problem.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2021)
Article
Mathematics, Interdisciplinary Applications
Wenbo Qiu, Jianghan Zhu, Huangchao Yu, Mingfeng Fan, Lisu Huo
Summary: This paper aims to improve a decomposition-based algorithm by designing an adaptive reference vector adjustment strategy. An improved angle-penalized distance (APD) method is developed to better distinguish solutions with sound convergence performance in each subspace.
Article
Energy & Fuels
Zhiguo Tang, Zhijian Zhao, Yongtao Ji, Jianping Cheng
Summary: Adopting a lightweight parallel liquid cooling structure with slender tubes and a thin heat-conducting plate, this study aims to meet the thermal management requirements of prismatic battery modules. The multiobjective optimization of structure, operating parameters, and thermal characteristics of battery modules is conducted. The effects of the equivalent diameter and inner diameter of tubes on various thermal metrics are investigated, and the optimization is performed using computational fluid dynamics and the NSGA-II algorithm.
JOURNAL OF ENERGY ENGINEERING
(2023)
Article
Geochemistry & Geophysics
Xiaohua Xu, Zhanghai Ju, Jia Luo
Summary: In this simulation study, operational GNSS satellites are used for global navigation satellite system reflectometry (GNSS-R) measurement. Different constellations of satellites are designed and optimized using multiobjective evolutionary algorithms. The optimal constellations show similar performance in terms of coverage and revisited coverage with specific inclinations and orbital altitudes.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Shufen Qin, Chaoli Sun, Yaochu Jin, Ying Tan, Jonathan Fieldsend
Summary: This article proposes a large-scale multiobjective evolutionary algorithm assisted by directed sampling, which selects individuals closer to the ideal point for reproduction to improve convergence. It also adopts elitist nondominated sorting and a reference vector-based method for environmental selection in order to maintain population diversity. Experimental results demonstrate the competitiveness of the proposed algorithm on large-scale multiobjective optimization problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.