Article
Computer Science, Artificial Intelligence
Dong Liu, Hao He, Qiang Yang, Yiqiao Wang, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a simple and effective mutation scheme named DE/current-to-rwrand/1 to enhance the optimization ability of differential evolution (DE) in solving complex optimization problems. The proposed mutation strategy, called function value ranking aware differential evolution (FVRADE), balances high diversity and fast convergence of the population. Experimental results demonstrate that FVRADE outperforms several state-of-the-art methods and shows promise in solving real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
Xiangbing Zhou, Xing Cai, Hua Zhang, Zhiheng Zhang, Ting Jin, Huayue Chen, Wu Deng
Summary: In this paper, a multi-strategy competitive-cooperative co-evolutionary algorithm named MSCOEA is proposed to effectively solve multi-objective optimization problems and balance uniformity and convergence. The MSCOEA incorporates a new adaptive random competition strategy to maintain diversity in sub-populations, and a neighborhood crossover strategy to enhance local search ability. Experimental results on different benchmark functions demonstrate that the MSCOEA achieves better optimization performance and robustness compared to other algorithms, while effectively balancing convergence and uniformity. The convergence performance of the adaptive random competition and neighborhood crossover strategies is also analyzed.
INFORMATION SCIENCES
(2023)
Article
Mathematics
Mengnan Tian, Yanghan Gao, Xingshi He, Qingqing Zhang, Yanhui Meng
Summary: This paper proposes a new variant of the differential evolution (DE) algorithm to mitigate its drawbacks such as premature convergence and stagnation. It introduces a novel mutation operator and a group-based competitive control parameter setting. The new mutation operator determines the scope of guidance based on the individual's fitness value. The competitive control parameter setting divides the population into equivalent groups and updates the worst location information with the current successful parameters. The proposed algorithm also incorporates a piecewise population size reduction mechanism to enhance exploration and exploitation at different stages. Experimental results demonstrate the superiority of the proposed method compared to other DE variants and non-DE algorithms.
Article
Computer Science, Information Systems
Qiang Yang, Jia-Qi Yan, Xu-Dong Gao, Dong-Dong Xu, Zhen-Yu Lu, Jun Zhang
Summary: This paper proposes a random neighbor elite guided differential evolution (RNEGDE) algorithm to effectively solve optimization problems. It introduces a novel mutation strategy named DE/current-to-rnbest/1, which randomly selects neighbors and uses elite guidance to direct individuals to promising areas. The algorithm also utilizes Gaussian and Cauchy distributions to generate adaptive parameter values for each individual. Extensive experiments show that the proposed algorithm achieves highly competitive or even better performance compared to state-of-the-art methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Heba Abdel-Nabi, Mostafa Z. Ali, Arafat Awajan, Rami Alazrai, Mohammad I. Daoud, Ponnuthurai N. Suganthan
Summary: This paper proposes a novel evolutionary algorithm, Ic3-aDSF-EA, which combines the exploitative and explorative merits of two main evolutionary algorithms, Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. The algorithm gradually emphasizes the work of the best-performing algorithm during the search process without ignoring the effects of other inferior algorithms.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Jianrui Wang, Yitian Hong, Jiali Wang, Jiapeng Xu, Yang Tang, Qing-Long Han, Jurgen Kurths
Summary: This article surveys the issues of cooperative optimization and games in multi-agent systems. It summarizes the research on distributed optimization and federated optimization from the perspective of cooperative optimization. It also introduces cooperative games and non-cooperative games to model the cooperative and non-cooperative behaviors of agents. Finally, it discusses future directions for research in cooperative optimization and games.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Mathematics
Tian-Tian Wang, Qiang Yang, Xu-Dong Gao
Summary: This paper proposes a dual elite groups-guided mutation strategy called DE/current-to-duelite/1 to solve complex optimization problems in continuous optimization. By guiding the mutation of all individuals using both the elites in the current population and the obsolete parent individuals stored in an archive, DEGGDE achieves a good balance between exploring the complex search space and exploiting the found promising regions, resulting in good optimization performance.
Article
Computer Science, Artificial Intelligence
The -Viet Ha, Quoc-Hung Nguyen, Tan -Tien Nguyen
Summary: This study proposes a parallel differential evolution algorithm (PDECMS) accelerated by utilizing a GPU to shorten the execution time of optimization algorithms for complex structural design problems. The results demonstrate that the algorithm achieves comparable solution quality, convergence speed, and scalability to other methods, while being at least twice as fast in computing time compared to its serial implementation.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Nianyin Zeng, Dandan Song, Han Li, Yancheng You, Yurong Liu, Fuad E. Alsaadi
Summary: The paper proposes a competitive mechanism integrated whale optimization algorithm (CMWOA) for multi-objective optimization problems. By introducing a novel competitive mechanism and improving the calculation of crowding distance, the convergence and accuracy of the algorithm are enhanced. Additionally, concatenating differential evolution (DE) into the population with different adjusting strategies for key parameters further improves the overall performance.
Article
Computer Science, Artificial Intelligence
LiBao Deng, Haili Sun, Chunlei Li
Summary: JDF-DE, a new variant based on Differential Evolution, achieves superior performance in global numerical optimization of complex high-dimensional problems by introducing improved parameter approach and crossover strategy.
APPLIED INTELLIGENCE
(2021)
Article
Mathematics
Qiang Yang, Xu Guo, Xu-Dong Gao, Dong-Dong Xu, Zhen-Yu Lu
Summary: This paper proposes a differential elite learning particle swarm optimization (DELPSO) method to guide the update of each particle by differentiating the two guiding exemplars. The method achieves good optimization performance when dealing with complicated optimization problems.
Article
Computer Science, Artificial Intelligence
Zhenyu Meng, Yuxin Chen
Summary: The paper introduces a new perspective that DE variants with exponential crossover can achieve competitive performance in numerical optimization, regardless of linkages among variables. It also presents a new powerful DE variant algorithm, DE-EXP, and a novel parameter control method for exponential crossover, demonstrating competitiveness in numerical optimization.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yanchi Li, Wenyin Gong, Shuijia Li
Summary: Competitive multitasking optimization (CMTO) is a special paradigm aiming to find an optimal solution for multiple tasks. Existing algorithms for CMTO problems have poor performance due to incorrect task selection in their resource allocation strategies. To address this, this paper proposes an improved multitasking adaptive differential evolution that features a success-history based resource allocation strategy, an adaptive random mating probability control strategy, and an adaptive multitasking differential evolution operator. Evaluation on benchmark test suites and real-world optimization problems shows that the proposed method achieves better performance empirically compared to other methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad H. Nadimi-Shahraki, Hoda Zamani
Summary: This study proposes an improved multi-trial differential evolution algorithm for solving non-decomposition large-scale global optimization problems, and introduces a diversity-maintained mechanism to prevent premature convergence. Through multiple experiments and tests, the DMDE algorithm demonstrates superior performance compared to competitor algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xueqing Yan, Mengnan Tian
Summary: This paper presents a novel differential evolution algorithm that utilizes prediction and adaptive mechanisms to improve search efficiency and balance exploration and exploitation, demonstrating better performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Hua Yang, Guo-Ping Jiang, Wallace K. S. Tang, Guanrong Chen, Ying-Cheng Lai
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2018)
Article
Engineering, Electrical & Electronic
Qiang Jia, Wallace K. S. Tang
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2018)
Article
Engineering, Electrical & Electronic
Qiang Jia, Wallace K. S. Tang
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2018)
Article
Engineering, Electrical & Electronic
Qiang Jia, Zeyu Han, Wallace K. S. Tang
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2019)
Article
Engineering, Civil
Xiaowen Bi, Wallace K. S. Tang
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2019)
Article
Physics, Multidisciplinary
Jianfeng Zhou, Zhong-yan Fan, Kai-Tat Ng, Wallace K. S. Tang
NEW JOURNAL OF PHYSICS
(2019)
Article
Engineering, Electrical & Electronic
Xiaowen Bi, Wallace K. S. Tang
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2020)
Article
Automation & Control Systems
Zeyu Han, Qiang Jia, Wallace K. S. Tang
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2020)
Article
Engineering, Electrical & Electronic
Zeyu Han, Wallace K. S. Tang, Qiang Jia
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2020)
Article
Computer Science, Artificial Intelligence
Qiang Jia, Eric S. Mwanandiye, Wallace K. S. Tang
Summary: This paper investigates the master-slave synchronization problem of delayed neural networks with general time-varying control. The main theorem is established in terms of the time average of the control gain by using the Lyapunov-Razumikhin theorem, and some useful corollaries are deduced. The theorem also provides a solution for regaining stability under control failure, which is further demonstrated with numerical examples.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Qiang Jia, Mei Sun, Wallace K. S. Tang
Summary: This paper focuses on the consensus problem of networked nonlinear agents with multiple self-delays and time-varying coupling. It establishes sufficient conditions for consensus and provides an estimation of the largest admissible delay, as well as useful criteria for various applications. The results have been verified with numerical simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Xiaowen Bi, Andrew John Chipperfield, Wallace K. S. Tang
Summary: The two-stage stochastic programming model proposed in the study can find high-quality charger allocation and optimal flow distribution policies under different traffic conditions, preventing over-investment on charging resources.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Automation & Control Systems
Qiang Jia, Wallace K. S. Tang
Summary: This work investigates the tracking consensus problem of multiagent systems over directed networks, and proposes event-based consensus protocols. By using an extended differential inequality, criteria for tracking consensus under time- and state-dependent triggering conditions are constructed. It is proved that the time average of the control gain, together with the agent dynamics, network topology, and triggering conditions, governs the consensus despite the fluctuation of control gain.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Electrical & Electronic
Qiang Jia, Zeyu Han, Wallace K. S. Tang
Summary: This work investigates the synchronization problem in a dynamical network with nonlinear nodes, directed couplings, and heterogeneous delays. The authors propose a time-varying pinning control strategy and introduce an indicator to measure the synchronizability of the network. Criteria are derived to ensure exponential synchronization and estimate the maximum admissible coupling delay. Numerical examples are provided to demonstrate the applicability of the proposed theorem and corollaries.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(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)
