Article
Automation & Control Systems
Ye Tian, Langchun Si, Xingyi Zhang, Kay Chen Tan, Yaochu Jin
Summary: This article proposes a novel PF estimation approach based on local models to address the difficulties in handling irregular PFs. By dividing the population into groups and building a local model for each group, the proposed approach can approximate PFs with complex geometrical structures. Experimental results show that the proposed algorithm outperforms the compared algorithms, especially on problems with highly irregular PFs.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Juanjuan Luo, Huadong Ma, Dongqing Zhou
Summary: The paper proposes a spectral clustering method based on multiobjective evolutionary algorithm, effectively determining the nonzero entries and values in the similarity matrix through a phased approach. By optimizing diversity and similarity, it achieves a balance between time cost and clustering accuracy as demonstrated in experiments.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Susan Wei, Marc Niethammer
Summary: This study provides a formal tool to address the tension between accuracy and fairness in algorithmic fairness. By using the concept of Pareto optimality, the fairness-accuracy Pareto front of a neural network classifier is identified. Compared to existing linear scalarization schemes, the Chebyshev scalarization scheme theoretically proves to be superior in recovering Pareto optimal solutions.
STATISTICAL ANALYSIS AND DATA MINING
(2022)
Article
Chemistry, Multidisciplinary
Shuo Liu, Hao Wang, Yong Cai
Summary: This study addresses the multiobjective optimization problem of irregular objects in the field of aquatic product processing by developing a simulated annealing algorithm. The algorithm's mutation strategy was optimized, and its effectiveness was demonstrated through experiments.
APPLIED SCIENCES-BASEL
(2021)
Article
Automation & Control Systems
S. Alireza Davari, Vahab Nekoukar, Cristian Garcia, Jose Rodriguez
Summary: This article introduces an online weighting factor optimization method based on the simulated annealing algorithm, which converges in a few steps using ripple energy as a convergence criterion and does not require cumbersome computations. It is applicable for an induction motor as well as other applications, and has been validated through experimental tests.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Mathematics
Carine M. Rebello, Marcio A. F. Martins, Daniel D. Santana, Alirio E. Rodrigues, Jose M. Loureiro, Ana M. Ribeiro, Idelfonso B. R. Nogueira
Summary: This work presents a novel approach for multiobjective optimization problems, introducing the concept of the Pareto region to efficiently portray optimal conditions. By applying a clustering strategy, a balanced approach between objectives can be achieved, providing valuable insights for decision-making in process optimization. Benchmark results have shown the effectiveness of the proposed method in illustrating Pareto regions, demonstrating its potential impact on processes optimization and operation decision-making.
Article
Engineering, Chemical
Jian Wang, Wenwei Chen, Yan Li, Jin Xu, Weifang Yu, Ajay K. Ray
Summary: The study investigates the performance of SMBR in reversible reactions and finds that both kinetics and adsorptive separation are important factors. Non-isothermal operation can significantly improve unit throughput under certain conditions, while feed concentration and reaction enthalpy have minor effects on the optimal solutions. Decision variables based on flow rate ratios and internal concentration profiles can explain the trends of Pareto optimal solution.
Article
Computer Science, Information Systems
Mario Garza-Fabre, Aaron L. Sanchez-Martinez, Edwin Aldana-Bobadilla, Ricardo Landa
Summary: Evolutionary multiobjective algorithms are popular for clustering problems due to their ability to optimize multiple criteria and their robustness to changes in data characteristics. This paper proposes a learning-based approach to decision making in clustering by building a model that can estimate solution quality and facilitate the selection of the best choice. Experimental results demonstrate the effectiveness of this approach compared to existing decision-making strategies.
Article
Computer Science, Artificial Intelligence
Julian Lee, David Perkins
Summary: The paper introduces a new clustering algorithm, SAGMDE, based on simulated annealing with two perturbation methods for large and small disturbances. Experimental results on various datasets show that SAGMDE performs more consistently in terms of cluster quality compared to existing clustering algorithms. Visual comparisons using generative art are used to evaluate different clustering algorithms.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Jhoseph Jesus, Anne Canuto, Daniel Araujo
Summary: Feature selection is crucial in machine learning, using multiple criteria to determine the best attribute subset can yield encouraging results. In data noise scenarios, the pareto-front based dynamic feature selection (PF-DFS) method shows more stability and robustness compared to other methods.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Yundong Tang, Hang Su, Tao Jin, Rodolfo Cesar Costa Flesch
Summary: Magnetic nanofluid hyperthermia (MNH) is a method that uses magnetic nanoparticles (MNPs) exposed to an alternating magnetic field to generate heat and damage malignant cells. This study proposes a control method for an MNH system using a proportional-integral-derivative (PID) control algorithm and dynamically optimizing the PID coefficients with a simulated annealing (SA) algorithm. The proposed system effectively modulates the power dissipation of MNPs and accurately regulates the treatment temperature to the desired value, while also adapting to changes in the nanofluid concentration distribution during therapy.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Yunhe Wang, Xiangtao Li, Ka-Chun Wong, Yi Chang, Shengxiang Yang
Summary: This article proposes two novel evolutionary multiobjective clustering algorithms with ensemble to address patient stratification problems, and demonstrates the effectiveness and competitive edges of the algorithms through experiments on synthetic and real datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Elham Azhir, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Arash Sharifi, Aso Darwesh
Summary: The query optimizer is responsible for identifying the most efficient Query Execution Plans (QEP's) and reusing previously generated plans is an efficient technique for query processing. To improve accuracy, queries are rewritten and converted into vectors. A multi-objective automatic query plan recommendation method has been introduced, optimizing cluster validity indices.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Interdisciplinary Applications
Weixun Yong, Jian Zhou, Danial Jahed Armaghani, M. M. Tahir, Reza Tarinejad, Binh Thai Pham, Van Van Huynh
Summary: This research develops three soft-computing techniques for predicting the ultimate-bearing capacity of a pile, with the SA-GP model performing the best in terms of correlation coefficient and mean square error. The pile's Q(ult) is most affected by the pile cross-sectional area and pile set.
ENGINEERING WITH COMPUTERS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoxia Han, Yingchao Dong, Lin Yue, Quanxi Xu, Gang Xie, Xinying Xu
Summary: In this article, a novel multi-objective optimization algorithm MOSTASA is proposed, which combines state-transition operators and the concept of Pareto dominance to generate and store Pareto optimal solutions, achieving a uniform distribution of solutions. Simulation experiments show that MOSTASA outperforms other algorithms in terms of efficiency and reliability.
APPLIED INTELLIGENCE
(2021)
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)
