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Computer Science, Artificial Intelligence
Amarjeet Prajapati
Summary: In this study, the performance of nine large-scale multi-objective optimization optimizers was evaluated and compared over five large-scale many-objective software clustering problems. The results showed that S3-CMA-ES and LMOSCO performed better in most cases, while H-RVEA was the worst performer.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Juan Zou, Jing Liu, Jinhua Zheng, Shengxiang Yang
Summary: This paper proposes a multi-objective optimization algorithm based on staged coordination selection, consisting of convergence and diversity stages. The algorithm aims to balance convergence and diversity in evolutionary algorithms, showing improved performance compared to existing algorithms on various benchmark instances.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
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Computer Science, Information Systems
Jinlong Zhou, Juan Zou, Shengxiang Yang, Jinhua Zheng, Dunwei Gong, Tingrui Pei
Summary: This paper proposes niche-based and angle-based selection strategies for many objective evolutionary optimization, which have been shown to be competitive and scalable to handle constrained many-objective optimization problems in experimental studies.
INFORMATION SCIENCES
(2021)
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Automation & Control Systems
Huangke Chen, Ran Cheng, Witold Pedrycz, Yaochu Jin
Summary: This paper proposes a method to solve multiobjective optimization problems through multi-stage evolutionary search, highlighting convergence and diversity in different search stages. The algorithm balances and addresses the issues in multiobjective optimization through two stages.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mohammadali Saniee Monfared, Sayyed Ehsan Monabbati, Atefeh Rajabi Kafshgar
Summary: This paper discusses noncooperative multi-objective optimization problems where the objective holders are independent humans or human-based entities, suggesting a new solution concept of the Pareto-optimal Equilibrium point. The interplay between game problems and multi-objective optimization problems is investigated, with illustrative examples provided to deepen the understanding of when a POE solution is achievable.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
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Computer Science, Artificial Intelligence
Juan Zou, Zhenghui Zhang, Jinhua Zheng, Shengxiang Yang
Summary: This paper proposes a co-guided evolutionary algorithm that combines the merits of dominance and decomposition to balance convergence and diversity in the evolutionary process. Experimental results demonstrate the superiority and versatility of this method in MaOPs.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Shuwei Zhu, Lihong Xu, Erik D. Goodman, Zhichao Lu
Summary: The article proposes a new multiobjective evolutionary algorithm based on the generalization of Pareto optimality, which uses the (M-1)-GPD framework to promote both convergence and diversity. Research shows that this algorithm is competitive on various benchmark problems and outperforms other methods on three real-world problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Qiqi Liu, Yaochu Jin, Martin Heiderich, Tobias Rodemann
Summary: A new surrogate-assisted evolutionary algorithm is proposed in this study to handle expensive irregular multi-objective optimization problems. The algorithm balances convergence and diversity by adapting reference vectors and implementing a surrogate management strategy, effectively taking irregularity of the Pareto front into account.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hossam M. J. Mustafa, Masri Ayob, Hisham A. Shehadeh, Sawsan Abu-Taleb
Summary: This paper proposes a multi-objective memetic differential evolution algorithm (MOMDE) for text clustering. The algorithm combines memetic and differential evolution algorithms to improve the search for optimal clustering by balancing exploitation and exploration. Experimental results show that the MOMDE algorithm outperforms state-of-the-art text clustering algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
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Computer Science, Artificial Intelligence
Saul Zapotecas-Martinez, Carlos A. Coello Coello, Hernan E. Aguirre, Kiyoshi Tanaka
Summary: Despite extensive studies on multi-objective test problem construction, researchers have mostly focused on designing complex search spaces, neglecting the design of Pareto optimal fronts. This paper introduces a scalable set of continuous and box-constrained multi-objective test problems, with unique Pareto fronts and features complicating the exploration of optimal solutions. The test suite provides components that can be used to construct new test instances and allows for analysis of multi-objective evolutionary algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Operations Research & Management Science
Oumayma Bahri, El-Ghazali Talbi
Summary: This paper focuses on addressing the robustness of multi-objective optimization problems with uncertain input data, specifically looking at the propagation of fuzziness to multiple objectives. New robustness techniques are introduced to combine fuzziness and the multi-objective context in order to maintain efficiency of fuzzy-valued objective values.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Automation & Control Systems
Vikas Palakonda, Jae-Mo Kang
Summary: This article proposes a preference-inspired differential evolution algorithm for multi and many-objective optimization, which effectively deals with a wide range of problems. The algorithm generates individuals with good convergence and distribution properties by utilizing a preference-inspired mutation operator and determining local knee points based on a clustering method. Experimental results demonstrate its superior performance compared to eight state-of-the-art algorithms on 35 benchmark problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Gaurav Dhiman, Mukesh Soni, Hari Mohan Pandey, Adam Slowik, Harsimran Kaur
Summary: A novel hybrid many-objective evolutionary algorithm H-RVEA is proposed and compared with five state-of-the-art algorithms, showing superior performance. Experimental results validate the effectiveness of the algorithm in solving real-life many-objective problems.
ENGINEERING WITH COMPUTERS
(2021)
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Computer Science, Artificial Intelligence
Chunliang Zhao, Yuren Zhou, Yuanyuan Hao, Guangyu Zhang
Summary: This paper introduces a new decomposition-based evolutionary algorithm with a bi-layer decision strategy for solving many-objective optimization problems. It accelerates convergence with adaptive fitness assignment and balances solution diversity with a diversity metric, resulting in better optimization results.
APPLIED INTELLIGENCE
(2022)
Article
Mathematics
Lining Xing, Rui Wu, Jiaxing Chen, Jun Li
Summary: This study proposes a novel many-objective evolutionary algorithm called LSEA to tackle the weakness of evolutionary many-objective algorithms based on decomposition. The LSEA performs local searches on an external archive to improve both convergence and diversity. Additionally, it perturbs the decision variables of selected solutions in order to search for better diversity and convergence. Experimental results on widely-used benchmarks demonstrate the competitive performance of the LSEA.
Article
Automation & Control Systems
Tapas Bhadra, Saurav Mallik, Sanghamitra Bandyopadhyay
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2019)
Article
Biochemical Research Methods
Saurav Mallik, Sanghamitra Bandyopadhyay
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2020)
Article
Energy & Fuels
Monalisa Pal, Amr Alzouhri Alyafi, Stephane Ploix, Patrick Reignier, Sanghamitra Bandyopadhyay
Article
Telecommunications
Priya Roy, Chandreyee Chowdhury, Dip Ghosh, Sanghamitra Bandyopadhyay
WIRELESS PERSONAL COMMUNICATIONS
(2019)
Article
Biology
Angana Chakraborty, Sanghamitra Bandyopadhyay
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2020)
Article
Computer Science, Artificial Intelligence
Snehalika Lall, Debajyoti Sinha, Abhik Ghosh, Debarka Sengupta, Sanghamitra Bandyopadhyay
Summary: The study introduces a feature selection algorithm based on copula that maximizes feature relevance and minimizes redundant information. The proposed CBFS algorithm competes well in maximizing classification accuracy on real and synthetic datasets and demonstrates better noise tolerance compared to other methods.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Monalisa Pal, Sanghamitra Bandyopadhyay
Summary: This paper introduces an evolutionary framework called LORD for addressing multi-modal multi-objective optimization problems (MMMOPs), which uses decomposition in both objective and decision space. The LORD-II algorithm further extends this framework, demonstrating its dynamics on multi-modal many-objective problems. The efficacy of the frameworks is established through performance comparisons with other algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Biochemical Research Methods
Angana Chakraborty, Burkhard Morgenstern, Sanghamitra Bandyopadhyay
Summary: The newly developed S-conLSH mapping tool uses spaced-context based Locality Sensitive Hashing to achieve faster mapping speed and higher sensitivity on 5 different real and simulated datasets. By utilizing multiple spaced patterns, S-conLSH enables gapped mapping of noisy long reads to the corresponding target locations of a reference genome, making it a promising direction towards alignment-free sequence analysis.
BMC BIOINFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Priya Roy, Chandreyee Chowdhury, Mausam Kundu, Dip Ghosh, Sanghamitra Bandyopadhyay
Summary: Indoor localization systems using WiFi signals face challenges due to the significant variation of signal strength with ambient conditions and device configuration. This paper proposes a weighted ensemble classifier based on Dempster-Shafer belief theory to efficiently handle context heterogeneity. Real life experiments show that the technique achieves high localization accuracy at varying granularity levels.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biochemical Research Methods
Sourav Biswas, Sumanta Ray, Sanghamitra Bandyopadhyay
Summary: This article introduces the concepts of network motifs and colored motifs, as well as a method to store colored subgraphs and discover colored motifs using a modified G-trie data structure. The approach utilizes approximate enumeration to reduce runtime and has been applied to find colored motifs in a host pathogen protein-protein interaction network. The study discovered eight motifs, with a majority containing both HIV-1 and human proteins.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Snehalika Lall, Sumanta Ray, Sanghamitra Bandyopadhyay
Summary: The study introduces a method RgCop based on regularized copula for stable and predictive gene selection in large-scale single cell RNA sequencing data, improving clustering/classification performance and enhancing the robustness of the method.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Biochemical Research Methods
Snehalika Lall, Sumanta Ray, Sanghamitra Bandyopadhyay
Summary: Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. sc-CGconv is an unsupervised feature extraction and clustering approach that utilizes copula correlation and graph convolution network to formulate and aggregate cell-cell relationships, which can identify homogeneous clusters with small sample sizes, model the expression co-variability of a large number of genes, preserve cell-to-cell variability, and provide a topology-preserving embedding of cells in low dimensional space.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Sumanta Ray, Snehalika Lall, Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Alexander Schoenhuth
Summary: This article introduces the use of artificial intelligence and deep learning techniques to screen drug repositories and find therapeutic options against COVID-19. By constructing a comprehensive molecular interaction network and predicting connections between drugs and human proteins, novel host-directed therapy options are established, providing a new approach for fighting the virus.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Geochemistry & Geophysics
Monidipa Das, Soumya K. Ghosh, Sanghamitra Bandyopadhyay
Summary: This article proposes a MARINE model to address the catastrophic forgetting issue that neural networks encounter when trained in a sequential manner, particularly in the presence of a large degree of subregional variations or heterogeneity in spatial zones. MARINE demonstrates competitive results in spatio-temporal prediction tasks and outperforms other methods in avoiding catastrophic forgetting, especially in highly heterogeneous spatial environments.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Cybernetics
Koushik Mallick, Sanghamitra Bandyopadhyay, Subhasis Chakraborty, Rounaq Choudhuri, Sayan Bose
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2019)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
