Article
Computer Science, Artificial Intelligence
Xinye Cai, Yushun Xiao, Zhenhua Li, Qi Sun, Hanchuan Xu, Miqing Li, Hisao Ishibuchi
Summary: This article proposes a kernel-based indicator (KBI) for evaluating Pareto front approximations generated by multi/many-objective optimizers. Unlike other distance-based indicators, KBI not only reflects the distance between the solution set and the reference set but also can reflect the distribution of the solution set itself. In addition, a non-dominated set reconstruction approach is proposed to maintain the desirable weak Pareto compliance property of KBI.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Jiajun Zhou, Shijie Rao, Liang Gao, Chao Lu, Jun Zheng, Felix T. S. Chan
Summary: This paper proposes a novel bi-partition assisted environmental selection strategy for addressing the complexities of the Pareto front in many-objective optimization problems. The strategy uses exemplar selection and an adaptive scalarizing function to handle diversity and convergence.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jiawei Yuan, Hai-Lin Liu, Fangqing Gu, Qingfu Zhang, Zhaoshui He
Summary: This article investigates the properties of ratio and difference-based indicators under the Minkovsky distance, and proposes an algorithm for solution evaluation using a ratio-based indicator. By identifying promising regions and ensuring population diversity, the algorithm demonstrates competitive performance on various benchmark problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Xujian Wang, Fenggan Zhang, Minli Yao
Summary: To handle the inconsistency between reference vectors (RVs) distribution and Pareto front shape in decomposition based multi-objective evolutionary algorithms, researchers have proposed various methods to adjust RVs during the evolutionary process. However, most existing algorithms adjust RVs either in each generation or at a fixed frequency without considering the evolving information of the population. To tackle this issue, the proposed MBRA algorithm adjusts RVs periodically and conditionally based on the improvement rate of convergence degree of subproblems computed through d(1) distance. Extensive experiments validate the effectiveness and competitiveness of MBRA on many-objective optimization problems, especially those with irregular Pareto fronts.
COMPLEX & INTELLIGENT SYSTEMS
(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
Automation & Control Systems
Kai Zhang, Chaonan Shen, Gary G. Yen
Summary: This article proposes a multipopulation-based differential evolution algorithm, called LSMaODE, to efficiently and effectively solve large-scale many-objective optimization problems. The algorithm divides the population into two groups of subpopulations and applies different optimization strategies. The performance is evaluated in both decision and objective dimensions.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Vikas Palakonda, Jae-Mo Kang, Heechul Jung
Summary: An ensemble approach combining mating and environmental selection operators of different MOEAs using AdaBoost and K-means clustering algorithms is proposed to enhance the performance of MOEAs on MaOPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Xujian Wang, Fenggan Zhang, Minli Yao
Summary: Research has shown that considering the convexity-concavity of Pareto fronts can improve the performance of multiobjective evolutionary algorithms (MOEAs). Based on this advantage, we propose a many-objective evolutionary algorithm, MaOEA-3C, which estimates the convexity-concavity of the Pareto fronts and incorporates clustering. The algorithm updates an elitist archive using non-dominated sorting and a niche-based method to estimate the convexity-concavity of the Pareto fronts and guides the evolving directions of the current population using clustering. The performance of MaOEA-3C is compared with seven state-of-the-art algorithms and demonstrates its effectiveness and competitiveness in many-objective optimization problems.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jonathan E. Fieldsend, Tinkle Chugh, Richard Allmendinger, Kaisa Miettinen
Summary: Visualizing the search behavior of a series of points or populations in their native domain is crucial for understanding biases and attractors in an optimization process. This study introduces a distance-based many-objective optimization test problem that allows for visualization of search behavior in a 2-D design space. The authors' previous work further advances this research by providing a problem generator that can automatically create user-defined problem instances with various problem features.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
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, Artificial Intelligence
Zhenan He, Gary G. Yen, Jinliang Ding
Summary: A novel knee-based decision-making method is proposed to search for solutions of interest from a large number of solutions on the Pareto front, ensuring the performance of these solutions approximates as much as possible the whole Pareto front. Additionally, a new visualization approach is developed to provide information about the shape, location, possible bulge, convergence degree, and distribution of solutions on MaOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Mathematics, Interdisciplinary Applications
Wen Zhong, Jian Xiong, Anping Lin, Lining Xing, Feilong Chen, Yingwu Chen
Summary: The study introduces a flexible ensemble framework ASES that enhances the performance of solving multi-objective optimization problems by embedding different MOEAs. By recording large-scale nondominated solutions in a big archive and developing an efficient strategy to update the archive, the efficiency of the algorithm is improved.
Article
Computer Science, Artificial Intelligence
Marina Torres, David A. Pelta, Maria T. Lamata, Ronald R. Yager
Summary: The focus is on selecting a set of interesting solutions based on decision maker's preferences, rather than relying on geometrical features. The proposed a posteriori approach assigns potential score intervals to each solution based on decision maker's preferences, and filters solutions using a possibility degree formula. Three examples with different numbers of objectives showcase the benefits of the proposal.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Operations Research & Management Science
Christian von Lucken, Carlos A. Brizuela, Benjamin Baran
Summary: This work presents a new multipopulation framework for the multiobjective evolutionary algorithm based on decomposition. Clustering methods are used to reinforce mating restrictions by splitting the population into multiple subpopulations for independent evolution. The results show the viability of the clustering-based multipopulation approach in enhancing the performance of evolutionary methods for many-objective problems.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hui Wang, Zichen Wei, Gan Yu, Shuai Wang, Jiali Wu, Jiawen Liu
Summary: This paper proposes a two-stage many-objective evolutionary algorithm, TS-DGPD, which accelerates convergence and maintains population diversity by using cosine distance and Lp norm, and increases selection pressure using dynamic generalized Pareto dominance. Experimental results show that the algorithm performs well in terms of convergence and diversity.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Ka-Chun Wong, Carlos A. Coello Coello, Jianqiang Li, Zhong Ming, Jun Zhang
Summary: This article proposes a self-guided RV strategy to extract adaptive RVs from the population, aiming to address the issue of insufficient matching between RV shapes and PFs in MaOEA/Ds algorithms. By using an angle-based density measurement strategy to obtain satisfactory clustering results, the effectiveness of this strategy is validated.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Geography
Jamshid Maleki, Zohreh Masoumi, Farshad Hakimpour, Carlos A. Coello Coello
Summary: This study aims to improve the efficiency of urban land use planning through enhancing the NSGA-III algorithm as a many-objective optimization approach. By considering five objective functions and testing the algorithm on real datasets, the results show enhanced convergence and diversity compared to traditional algorithms like NSGA-II. The optimized solutions obtained can aid decision-makers in achieving sustainable development in urban construction.
TRANSACTIONS IN GIS
(2022)
Article
Computer Science, Artificial Intelligence
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This study introduces a second variant of the successful RS-CMSA-ES method, called RS-CMSA-ESII, which improves upon certain components and enhances the performance of the method in multimodal optimization.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes a fuzzy decomposition-based MOEA that estimates the population's shape using fuzzy prediction and selects weight vectors to fit the Pareto front shapes of different multi-objective optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Lijia Ma, Xiao Zhang, Jianqiang Li, Qiuzhen Lin, Maoguo Gong, Carlos A. Coello Coello, Asoke K. Nandi
Summary: This article studies the robustness and resilience of multiplex networks in the presence of node cascading failures caused by coupling node relationships and community structures. A node protection strategy and a degree-based simulated annealing algorithm are proposed to improve network robustness and resilience. Experimental results show the vulnerability of networks to unpredictable damage under these circumstances, as well as the superiority of the proposed algorithm over existing ones.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Information Systems
Eduardo Fernandez, Jorge Navarro, Efrain Solares, Carlos A. Coello Coello, Raymundo Diaz, Abril Flores
Summary: Multicriteria sorting methods based on interval-based outranking approach are proposed, and parameter values are elicited using preference disaggregation paradigm and evolutionary algorithms. The proposed method effectively restores assignment examples and improves the ability to assign unknown actions.
Article
Automation & Control Systems
Lingjie Li, Qiuzhen Lin, Zhong Ming, Ka-Chun Wong, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an immune-inspired resource allocation strategy to better balance convergence and diversity in many-objective optimization. By defining the diversity distances of solutions, resource allocation is realized using an immune cloning operator to explore sparse regions of the search space. A novel archive update mechanism is also designed to provide high-quality solutions. The experimental results validate the superiority of this method in solving complex MOPs with 5 to 15 objectives.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Rihab Said, Maha Elarbi, Slim Bechikh, Carlos Artemio Coello Coello, Lamjed Ben Said
Summary: Discretization-based feature selection methods have a drawback of deleting important features during the encoding process. To address this issue, a bilevel optimization algorithm called Bi-DFS is proposed, which performs feature selection at the upper level and discretization at the lower level. Experimental results demonstrate that Bi-DFS outperforms existing methods in terms of classification accuracy, generalization ability, and feature selection bias.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Multidisciplinary Sciences
Joel Artemio Morales-Viscaya, Adan Antonio Alonso-Ramirez, Marco Antonio Castro-Liera, Juan Carlos Gomez-Cortes, David Lazaro-Mata, Jose Eleazar Peralta-Lopez, Carlos A. Coello A. Coello, Jose Enrique Botello-Alvarez, Alejandro Israel Barranco-Gutierrez
Summary: Fuzzy systems are widely used due to their robust, accurate, and easy-to-evaluate models that capture real-world uncertainty better than classical alternatives. This study proposes a new methodology for tuning fuzzy models using optimization techniques, resulting in better performance than existing strategies. By considering symmetry and equispacing, the membership functions are simplified, and a gradient descent method is used for optimization. The proposed strategy outperforms other methods, achieving at least 28% improvement in performance across all case studies in terms of RMSE.
Article
Automation & Control Systems
Lingjie Li, Yongfeng Li, Qiuzhen Lin, Songbai Liu, Junwei Zhou, Zhong Ming, Carlos A. Coello Coello
Summary: This research proposes a new neural net-enhanced competitive swarm optimizer (NN-CSO) to address the neglect of the evolution of winner particles in traditional CSOs. Experimental results show that NN-CSO significantly improves the performance of CSOs and outperforms other large-scale multiobjective evolutionary algorithms and model-based evolutionary algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Qiuzhen Lin, Zhongjian Wu, Lijia Ma, Maoguo Gong, Jianqiang Li, Carlos A. Coello Coello
Summary: This article proposes a new multiobjective multitasking evolutionary algorithm, MMTEA-DTS, which uses decomposition-based transfer selection. The algorithm decomposes all tasks into subproblems and quantifies the transfer potential of each solution based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected for knowledge transfer to improve search efficiency.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Antonio J. Nebro, Jesus Galeano-Brajones, Francisco Luna, Carlos A. Coello Coello
Summary: This study demonstrates the improved performance of the NSGA-II algorithm in large-scale optimization problems. By utilizing automated algorithmic tuning tools and a highly configurable version of NSGA-II, the experimental results show that the algorithm performs well in solving both test problems and real-world problems.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos Artemio Coello Coello
Summary: HDE is a multi-objective evolutionary algorithm that turns its selection process into a linear assignment problem. This study identifies two drawbacks in its selection process and proposes an algorithm using the hypervolume indicator to address these drawbacks.
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT II
(2022)
Article
Computer Science, Artificial Intelligence
Lijia Ma, Yuchun Ma, Qiuzhen Lin, Junkai Ji, Carlos A. Coello Coello, Maoguo Gong
Summary: In this paper, a novel generative adversarial nets learning framework (SNEGAN) is proposed for signed network embedding, preserving link structures signed by positive or negative labels. Extensive experiments demonstrate the superiority of SNEGAN over the state-of-the-art NE methods in link (sign) prediction and reconstruction tasks.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Rohan Mohapatra, Snehanshu Saha, Carlos A. Coello Coello, Anwesh Bhattacharya, Soma S. Dhavala, Sriparna Saha
Summary: AdaSwarm is a high-performance gradient-free optimizer that uses the novel EMPSO for gradient approximation, capable of handling various loss functions.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
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)
