Article
Multidisciplinary Sciences
Muhammad Hassan Baig, Qamar Abbas, Jamil Ahmad, Khalid Mahmood, Sultan Alfarhood, Mejdl Safran, Imran Ashraf
Summary: Symmetry plays an important role in solving differential equations using the differential evolution (DE) algorithm. This study introduces a new mutation strategy and algorithm, DE/Neighbor/2 and IRNDE, to improve the convergence speed and performance. Experimental results show that DE/Neighbor/2 and IRNDE have better and faster convergence compared to DE/Neighbor/1 and RNDE.
Article
Computer Science, Artificial Intelligence
Wu Deng, Shifan Shang, Xing Cai, Huimin Zhao, Yingjie Song, Junjie Xu
Summary: The paper introduces an improved differential evolution algorithm NBOLDE with neighborhood mutation operators and opposition-based learning, which accelerates convergence speed and enhances optimization capabilities by proposing a new neighborhood strategy and implementing a new neighborhood mutation strategy.
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
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
Multidisciplinary Sciences
Tassawar Ali, Hikmat Ullah Khan, Tasswar Iqbal, Fawaz Khaled Alarfaj, Abdullah Mohammad Alomair, Naif Almusallam
Summary: Differential evolution is an evolutionary algorithm that balances exploration and exploitation to find the optimal genes for an objective function. To address the challenge of finding this balance, a clustering-based mutation strategy called Agglomerative Best Cluster Differential Evolution (ABCDE) is proposed. ABCDE efficiently converges without getting trapped in local optima by clustering the population and avoiding poor-quality genes through adaptive crossover rate. ABCDE outperforms classical mutation strategies and random neighborhood mutation strategy in generating a population where the difference between the values of the trial vector and objective vector is less than 1% for some benchmark functions. The optimal and fast convergence of differential evolution has potential applications in weight optimization of artificial neural networks and stochastic/time-constrained environments like cloud computing.
Article
Computer Science, Information Systems
Yi Wang, Tao Li, Xiaojie Liu, Jian Yao
Summary: This study develops an improved adaptive clonal selection algorithm with multiple differential evolution strategies. The algorithm introduces an adaptive mutation strategy pool, an adaptive population resizing method, and detection methods for premature convergence and stagnation. Experimental results demonstrate that the proposed method outperforms state-of-the-art clonal selection algorithms and differential evolution algorithms.
INFORMATION SCIENCES
(2022)
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
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
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, Artificial Intelligence
Giovanni Iacca, Vinicius Veloso de Melo
Summary: Research shows that incorporating centroid calculation into DE mutation increases search effectiveness, and using centroids calculated by deterministic hierarchical clustering in mutation strategies outperforms traditional methods.
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
Computer Science, Information Systems
Wen-Xuan Ji, Qiang Yang, Xu-Dong Gao
Summary: This paper proposes a novel mutation approach called DE/current-to-gselite/1, which utilizes the Gaussian distribution to sample guiding exemplars around elites for effective optimization. Experimental results show that GSGDE has good scalability and achieves highly competitive performance in the latest CEC2014 and CEC2017 problem suites, outperforming 11 latest and representative DE methods, especially as the dimensionality increases.
Article
Computer Science, Artificial Intelligence
Anping Lin, Shanglin Li, Rongsheng Liu
Summary: This study proposes a mutual learning strategy to develop a high-performance hybrid algorithm based on particle swarm optimization and differential evolution. The proposed mutual learning differential evolution particle swarm optimization (MLDE-PSO) outperforms other algorithms in terms of performance, especially on rotated functions and CEC2017 functions.
EGYPTIAN INFORMATICS JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Tae Jong Choi, Julian Togelius, Yun-Gyung Cheong
Summary: The paper introduces an improved stochastic opposition-based learning algorithm called iBetaCOBL, which addresses the high computational cost and assumption of independent decision variables in BetaCOBL, showing excellent performance in comparison to other OBL variants in performance evaluations.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Zhiqiang Zeng, Min Zhang, Tao Chen, Zhiyong Hong
Summary: Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve the algorithm’s performance, especially when individuals are in a state of stagnation. Experimental results have shown that the proposed selection operator significantly enhances the algorithm's performance and helps it escape local optimal values.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Telecommunications
Bo Jiang, Guosheng Huang, Tian Wang, Jinsong Gui, Xiaoyu Zhu
Summary: In this article, a trust-based energy efficient data collection scheme using unmanned aerial vehicles is proposed, which optimizes trajectory and identifies trusted data to prolong network lifetime and improve data collection quality.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2022)
Article
Telecommunications
Ting Li, Wei Liu, Tian Wang, Zhao Ming, Xiong Li, Ming Ma
Summary: This study proposes a novel scheme named T-SIoTs, which utilizes trust vehicles and UAVs to establish a trust-based environment for data collection in the IoT. The scheme improves security and achieves efficient data collection through static stations and shortest-distance-first routing scheme.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2022)
Article
Computer Science, Theory & Methods
Yingying Ren, Wei Liu, Anfeng Liu, Tian Wang, Ang Li
Summary: The crowdsourcing scheme for data-based applications in the IoT network faces issues with malicious participants, which a proposed privacy-protected intelligent crowdsourcing scheme (PICRL) aims to address using reinforcement learning and trust evaluation mechanisms. The PICRL optimizes utility through effective trust assessment and Q-learning method without prior knowledge of specific sensing models.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Automation & Control Systems
Tian Wang, Haoxiong Ke, Alireza Jolfaei, Sheng Wen, Mohammad Sayad Haghighi, Shuqiang Huang
Summary: With the development of 5G technology and Internet of Things, incomplete real life data can be efficiently processed using edge computing methods in the AIoT environment. This approach outperforms other filling methods in terms of quality and reduces energy consumption significantly.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Yang Xu, Md Zakirul Alam Bhuiyan, Tian Wang, Xiaokang Zhou, Amit Kumar Singh
Summary: In this article, we propose a framework called C-fDRL to protect the context-aware privacy of task offloading using context-aware federated deep reinforcement learning. The framework operates in three stages (CloudAI, EdgeAI, and DeviceAI) of the overall system, decoupling data from tasks through a context-aware data management approach for local and edge computation, leading to improved data privacy protection.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Liang Wang, Zhiwen Yu, Kaishun Wu, Dingqi Yang, En Wang, Tian Wang, Yihan Mei, Bin Guo
Summary: Mobile Crowdsensing (MCS) is an appealing paradigm for collaboratively collecting data from surrounding environments by assigning outsourced sensing tasks to volunteer workers. However, unpredictable disruptions during task implementation often result in task execution failure and impair the benefit of MCS systems. In this work, we propose a robust task assignment scheme that proactively creates assignments offline, aiming to strengthen the robustness of the scheme and minimize workers' traveling detour cost. By leveraging workers' spatiotemporal mobility, we construct an assignment graph and use an evolutionary multi-tasking optimization algorithm (EMTRA) to achieve adequate Pareto-optimal schemes. Comprehensive experiments on real-world datasets validate the effectiveness and applicability of our approach.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Miaojiang Chen, Wei Liu, Tian Wang, Shaobo Zhang, Anfeng Liu
Summary: This paper proposes a polling callback energy-saving offloading strategy, and simulation results show that the proposed algorithm performs better than DDQN, DQN, and BCD-based optimal methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Jing Bai, Zhiwen Zeng, Tian Wang, Shaobo Zhang, Neal N. Xiong, Anfeng Liu
Summary: In this article, a trust-based active notice task offloading (TANTO) scheme is proposed to provide trust and low-delay task offloading for resource-limited IoT devices in areas with no available communication infrastructure. The main innovations of TANTO include a novel task offloading mechanism, a trust calculation and reasoning method, and an online UAV trajectory optimization algorithm. Experimental results show that TANTO outperforms previous studies in terms of task completion rate, tasks' average completion time, and UAV's flight cost.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Cybernetics
Tian Wang, Xuewei Shen, Mohammad S. Obaidat, Xuxun Liu, Shaohua Wan
Summary: For smart cities, ubiquitous user connectivity and collaborative computation offloading are significant. This article proposes an information prefetching architecture that optimizes collaborative edge computing using a hierarchical data storage and selection strategy. It also utilizes an independent/joint edge-learning model to improve algorithm efficiency and cost-effectiveness.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Article
Automation & Control Systems
Tian Wang, Yilin Zhang, Neal N. Xiong, Shaohua Wan, Shigen Shen, Shuqiang Huang
Summary: With the rapid development of wireless communication, traditional cloud computing is not sufficient for low-latency services. Mobile edge computing (MEC) can enhance the user experience and reduce energy consumption. In this article, an edge-intelligent service placement algorithm (EISPA) is proposed, which utilizes nature-inspired particle swarm optimization (PSO) to find the global optimal solution. The algorithm also incorporates a shrinkage factor and simulated annealing (SA) to avoid falling into local optima. Performance analysis results demonstrate that the EISPA outperforms other algorithms in terms of system cost under energy constraints.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Hardware & Architecture
Wenzheng Xu, Yueying Sun, Rui Zou, Weifa Liang, Qiufen Xia, Feng Shan, Tian Wang, Xiaohua Jia, Zheng Li
Summary: This paper studies the deployment of multiple UAVs to provide emergent communication services, proposing a novel problem and improving the current best solution by five times with a new approximation algorithm. Experimental results show that the proposed algorithm is very promising, delivering solutions up to 12% better than existing algorithms.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2022)
Article
Telecommunications
Linbo Deng, Jinsong Gui, Tian Wang, Jiawei Tan, Xiong Li
Summary: This paper proposes a Maximum Listening Length MAC (MLL-MAC) protocol to address the energy limitations in medical sensors. By sending beacons and prolonging the listening duration, the protocol reduces the delay and improves energy utilization.
DIGITAL COMMUNICATIONS AND NETWORKS
(2022)
Proceedings Paper
Computer Science, Software Engineering
Shangrui Wu, Yang Wang, Tian Wang, Weijia Jia, Ruitao Xie
Summary: Video saliency detection interprets the human visual system by modeling and predicting. The proposed SAFS module selects highly informative frames and has high robustness and extensive application. Combined with TASED-NET, our method achieves significant improvements on various datasets.
ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT II
(2022)
Article
Computer Science, Artificial Intelligence
Ying Ma, Yunjie Lei, Tian Wang
Summary: In this paper, the LC-KNN algorithm is proposed to improve the recognition accuracy of multi-label natural scene images by analyzing and weighting the correlation between instances. The experimental results show that the algorithm outperforms mainstream algorithms in multi-label natural scene recognition tasks.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Xuxun Liu, Peihang Lin, Tang Liu, Tian Wang, Anfeng Liu, Wenzheng Xu
Summary: This research proposes an objective-variable tour planning (OVTP) strategy for mobile data gathering in partitioned wireless sensor networks (WSNs), addressing the issue of complex network environments. The strategy focuses on disjoint networks with connectivity requirement and serves delay-hash applications as well as energy-efficient scenarios. Extensive simulations demonstrate the effectiveness and advantages of the new strategy in terms of path length, energy depletion, and data collection ratio.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(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)
