Article
Computer Science, Interdisciplinary Applications
Caiyang Yu, Mengxiang Chen, Kai Cheng, Xuehua Zhao, Chao Ma, Fangjun Kuang, Huiling Chen
Summary: The SGOA, an improved grasshopper optimization algorithm combining simulated annealing mechanism with the original GOA, outperformed other algorithms in various fields and engineering problems. With promising results in benchmark function testing and engineering applications, SGOA proves to be effective in solving complex optimization problems.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Artificial Intelligence
Qi You, Jun Sun, Vasile Palade, Feng Pan
Summary: This paper proposes a hybrid quantum-behaved particle swarm optimization algorithm (QPSO-DGS) with dynamic grouping searching strategy, which aims to solve the premature convergence issue in complex optimization problems. Experimental results show that QPSO-DGS has promising performance in terms of solution accuracy and convergence speed, especially on multimodal problems.
INTELLIGENT DATA ANALYSIS
(2023)
Article
Computer Science, Software Engineering
Bin Deng
Summary: This paper introduces an improved algorithm of the Aquila optimizer, called Dynamic Grouping Strategy (DGS). By evolving the individuals with the worst fitness in each group, and testing it on multiple benchmark functions and engineering design problems, it proves the effectiveness of the algorithm in solving global optimization problems.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Multidisciplinary Sciences
Yanjiao Wang, Jieru Han, Ziming Teng
Summary: This paper proposes an improved Group Teaching Optimization Algorithm (IGTOA) to enhance the convergence speed and accuracy. It assigns teachers independently to each individual, increasing the evolution direction and population diversity. It dynamically divides students into different groups to meet the needs of different evolutionary stages. Additionally, it improves the teaching method for average group students and proposes a population reconstruction mechanism.
SCIENTIFIC REPORTS
(2022)
Article
Automation & Control Systems
Song Liu, Shumin Zhou, Xiujuan Lu, Fang Gao, Feng Shuang, Sen Kuang
Summary: This paper presents a Lyapunov control scheme to drive finite-dimensional closed and Markovian open quantum systems into any target pure state with high fidelity and short time. The control law is established using a Lyapunov function and the optimal eigenvalues are searched using the quantum-behaved particle swarm optimization algorithm. A improved constrained QPSO algorithm is proposed for open systems with small denominator in the control law. Numerical simulations on different quantum systems demonstrate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Information Systems
Ahmad Taheri, Keyvan RahimiZadeh, Ravipudi Venkata Rao
Summary: TLBO algorithm simulates teaching and learning mechanisms in a classroom and has efficient optimization capabilities, but may converge to local optima in complex problems. The proposed BTLBO algorithm achieves a balance between exploration and exploitation capabilities through four phases.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
L. Phani Raghav, R. Seshu Kumar, D. Koteswara Raju, Arvind R. Singh
Summary: The article introduces a stochastic framework utilizing the Quantum Teaching Learning-based optimization (QTLBO) algorithm to optimize energy flow in microgrids, assessing four scenarios of seasonal variations. Results show the superiority of QTLBO in terms of convergence and achieving global optimum solutions for microgrid optimization.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Computer Science, Information Systems
Xinming Zhang, Qiuying Lin
Summary: This paper proposes an improved SL-PSO algorithm, called TLS-PSO, which enhances the optimization performance of PSO through the use of three learning strategies and a hybrid learning mechanism. Experimental results demonstrate that TLS-PSO outperforms state-of-the-art PSO variants and other algorithms on complex functions and engineering problems, indicating its superior performance and potential for practical problem-solving.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Xinmin Tao, Wenjie Guo, Xiangke Li, Qing He, Rui Liu, Junrong Zou
Summary: This paper proposes a dynamic multi-swarm Particle Swarm Optimization algorithm, FPCMSPSO, based on fitness peak clustering to balance the tradeoff between exploration and exploitation. The algorithm utilizes enhanced learning strategy and partitioning method to improve solution accuracy, convergence speed and stability, outperforming other PSO variants statistically on various optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Xuefeng Wang, Jingwen Hu, Jiaoyan Hu, Yucheng Wang
Summary: Equilibrium Optimizer (EO) is an intelligent optimization algorithm with excellent solution effect and strong adaptability. However, it has some limitations when dealing with complex multimodal problems. To address these issues, a modified equilibrium optimizer (OTLEO) has been proposed and its superiority over the original EO and other algorithms has been demonstrated through testing and comparative evaluation.
Article
Mathematics
Yeerjiang Halimu, Chao Zhou, Qi You, Jun Sun
Summary: This paper proposes a quantum-behaved particle swarm optimization (QPSO) algorithm on Riemannian manifolds named RQPSO to solve the issues of non-convex manifold global convergence and non-differentiable mathematical models. Experimental results show that RQPSO outperforms traditional algorithms in terms of calculation speed and optimization efficiency.
Article
Computer Science, Artificial Intelligence
Ahmed M. Anter, Hany S. Elnashar, Zhiguo Zhang
Summary: This study proposes a new quantum-behaved multiverse optimization (QMVO) approach and a prediction method based on sparse coding and dictionary learning (SCDL) for handling high-dimensional and complex fMRI data. The proposed model, applied to pain perception fMRI data, demonstrates high accuracy in decoding pain levels and identifying predictive fMRI patterns. Moreover, the performance of the model surpasses that of other machine learning techniques.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Qi You, Jun Sun, Feng Pan, Vasile Palade, Bilal Ahmad
Summary: The paper integrates the QPSO algorithm with the MOEA/D framework to propose the DMO-QPSO algorithm, which improves performance through diversity control mechanisms and the introduction of non-dominated solutions. Experiments show that DMO-QPSO outperforms other algorithms in solving multi-objective problems.
Article
Chemistry, Multidisciplinary
Mahdi S. Alajmi, Abdullah M. Almeshal
Summary: The study explored the performance of machine learning and quantum evolutionary computation methods in predicting the surface roughness of aluminum material on a milling machine. QPSO and LSBoost accurately simulated numerous experiments and predicted surface roughness values with high accuracy, outperforming other algorithms.
APPLIED SCIENCES-BASEL
(2021)
Article
Multidisciplinary Sciences
Aijun Zhu, Zhanqi Gu, Cong Hu, Junhao Niu, Chuanpei Xu, Zhi Li
Summary: The political optimizer (PO) is a cutting-edge meta-heuristic optimization technique that simulates the multi-stage process of politics in human society, but suffers from stagnation in local optima due to certain drawbacks. Novel PO variants have been proposed by integrating interpolation strategies and Refraction Learning (RL) to enhance exploration capacity and balance between global exploration and local exploitation, leading to superior performance in terms of global optimization problems.
Article
Computer Science, Artificial Intelligence
Fang Zhu, Debao Chen, Feng Zou
Summary: A dynamic fireworks algorithm with particle swarm optimization (DFWPSO) is proposed in this paper to enhance the global performance of FWA by dynamically adjusting explosion amplitude and implementing a new update mechanism; experimental results demonstrate the competitive and effective performance of the algorithm in solving optimization problems.
Article
Computer Science, Artificial Intelligence
Feng Zou, Debao Chen, Qingzheng Xu, Ziqi Jiang, Jiahui Kang
Summary: In this paper, a two-stage personalized recommendation algorithm based on improved collaborative filtering and multi-objective optimization is proposed, aiming to enhance the accuracy and diversity of recommendations. Experimental results demonstrate the effectiveness and efficiency of the algorithm in personalized recommender systems.
Article
Computer Science, Artificial Intelligence
Yu Deng, Debao Chen, Feng Zou, Yuan Chen, Ying Zheng, Minglan Fu, Chun Wang
Summary: The paper proposes a framework of heterogeneous ensemble algorithms (EHA) that integrates multiple optimization methods with different structures. The EHA framework effectively utilizes the advantages of different algorithms without significantly increasing computational complexity. Evaluation results indicate that EHA has excellent optimization performance.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Yujie Wan, Minglan Fu, Lvqiang Chen, Debao Chen, Jiekun Li, Wei Zhou, Mengxue Liu
Summary: The task allocation process in the Witkey mode requires reaching a stable Nash equilibrium and achieving high total system revenue. This study proposes an incentive measure based on integral ranking to improve user participation.
ADVANCES IN MULTIMEDIA
(2022)
Correction
Computer Science, Artificial Intelligence
Yu Deng, Debao Chen, Feng Zou, Yuan Chen, Ying Zheng, Minglan Fu, Chun Wang
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Debao Chen, Yuanyuan Ge, Yujie Wan, Yu Deng, Yuan Chen, Feng Zou
Summary: This paper introduces a novel algorithm called Poplar Optimization Algorithm (POA) to solve continuous optimization problems by mimicking the sexual and asexual propagation mechanism of poplar. The algorithm shows competitive and superior performance in performance testing and successfully finds the optimal threshold for image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Yuanyuan Ge, Debao Chen, Feng Zou, MingLan Fu, Fangzhen Ge
Summary: The competitive swarm optimizer (CSO) is an efficient algorithm for solving larger-scale multiobjective optimization problems (LSMOPs). In this study, an adaptive competitive swarm optimizer with inverse modeling is proposed to improve the performance of CSO. The winners are updated using inverse modeling and an adjacent individual competition method is introduced to enhance the distribution of solutions. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms on benchmark optimization problems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Siyu Cao, Feng Zou, Debao Chen, Hui Liu, Xuying Ji, Yan Zhang
Summary: Dynamic multi-objective problems are prevalent in daily life and practical applications. This paper proposes a new hybrid prediction model (HPM) to solve these problems, and the results show that HPM outperforms other strategies in dynamic optimization.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ziqi Jiang, Feng Zou, Debao Chen, Siyu Cao, Hui Liu, Wei Guo
Summary: This paper proposes a new ensemble multi-swarm method based on teaching-learning-based optimization (EMTLBO), which integrates multiple algorithms to achieve better optimization performance. It introduces an evaluating mechanism and an algorithm matching mechanism to improve the overall optimization performance. The experimental results demonstrate the feasibility and effectiveness of EMTLBO, and it also shows good performance in image segmentation.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Feng Zou, Debao Chen, Hui Liu, Siyu Cao, Xuying Ji, Yan Zhang
Summary: Fitness landscape analysis (FLA), as a powerful analytical tool, has been widely applied in various optimization areas. It helps to gain a deep understanding of the characteristics of complex optimization problems and improve algorithm performance on specific problems.
Proceedings Paper
Computer Science, Theory & Methods
Xuying Ji, Feng Zou, Debao Chen, Yan Zhang
Summary: Fitness landscape is an evolutionary mechanism that can improve optimization performance by analyzing the fitness landscape. This paper introduces a new fitness-landscape-driven particle swarm optimization algorithm, characterizing the fitness landscape to improve optimization performance and introducing a selection mechanism for choosing better variants. Experimental results show that the proposed algorithm significantly improves optimization accuracy and convergence.
INTELLIGENT COMPUTING THEORIES AND APPLICATION (ICIC 2022), PT I
(2022)
Article
Engineering, Electrical & Electronic
Tao Sheng, Shengzhe Shi, Yuanyang Zhu, Debao Chen, Sheng Liu
Summary: This study presents a fast and accurate method for measuring milk composition using an infrared spectral sensor and machine learning algorithm. The results show that the proposed system can provide real-time, simple, and fast determination of milk protein and fat content.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Multidisciplinary Sciences
Yuan Chen, Debao Chen, Yu Deng, Feng Zou, Ying Zheng, Minglan Fu, Chun Wang
Summary: The study presents an information feedback model based on mutual information to directly handle multi-objective optimization problems, which can be easily integrated with any optimization algorithm, improving the balance between convergence and diversity in the population.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(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)
