Article
Computer Science, Artificial Intelligence
Ankita, Sudip Kumar Sahana
Summary: This paper proposes a new balanced PSO algorithm to solve the scheduling problem of computational grid. The algorithm is evaluated using a standard dataset, and its results outperform other considered deterministic and heuristic approaches.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yong Zhang, Xin-Fang Ji, Xiao-Zhi Gao, Dun-Wei Gong, Xiao-Yan Sun
Summary: This article introduces an objective-constraint mutual-guided surrogate-assisted particle swarm optimization algorithm for expensive constraint multimodal optimization problems. The algorithm utilizes a two-layer cooperative surrogate model framework and a partial evaluation strategy to reduce computational cost while obtaining multiple competitive feasible optimal solutions. It also proposes a hybrid update mechanism and a local search strategy to improve the algorithm's performance. Experimental results demonstrate the effectiveness of the proposed algorithm compared to existing methods.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Dang Cong Hop, Nguyen Van Hop, Truong Tran Mai Anh
Summary: This study proposes a solution to the integrated quay crane and yard truck scheduling problem, utilizing a mixed-integer programming model and Adaptive Particle Swarm Optimization algorithm to minimize the total time for unloading and transporting operations. The algorithm performs well in handling large-sized problems and obtaining closed optimal solutions.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Mathematics
Omer Ali, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri, Imran Ashraf
Summary: This study introduces a competitive coevolution process to enhance the capability of Phasor PSO (PPSO) for global optimization problems. Experimental results show that the improved competitive multi-swarm PPSO (ICPPSO) algorithm achieves a dominating performance, with average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO.
Article
Automation & Control Systems
Li Li, Liang Chang, Tianlong Gu, Weiguo Sheng, Wanliang Wang
Summary: This paper introduces a novel multiobjective PSO algorithm named MOPSO/DD, which utilizes the dominant difference norm to tackle MaOPs. Experimental results demonstrate that the algorithm is competitive with state-of-the-art approaches on benchmark problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Sonal Jain, Dharavath Ramesh, Diptendu Bhattacharya
Summary: This paper proposes a multi-objective optimization algorithm by combining two optimization algorithms to improve crop benefits and reduce fertilizer application, demonstrating feasibility and effectiveness in optimizing crop patterns.
APPLIED SOFT COMPUTING
(2021)
Article
Chemistry, Multidisciplinary
Sung-Jung Hsiao, Wen-Tsai Sung
Summary: This paper uses the approach of the differential model to effectively improve the analysis of particle swarm optimization, proposing a differential evolution PSO model. By adjusting parameters and using different transformation methods, the performance of the PSO algorithm is optimized.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Xiangyu Wang, Bingran Zhang, Jian Wang, Kai Zhang, Yaochu Jin
Summary: This paper proposes a novel cluster-based competitive particle swarm optimizer equipped with a sparse truncation operator for solving sparse multi-objective optimization problems. Experimental results show that the proposed algorithm outperforms its peers on sparse test instances and neural network training tasks.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Sudheer Mangalampalli, Sangram Keshari Swain, Vamsi Krishna Mangalampalli
Summary: Efficient task scheduling in cloud computing is crucial in minimizing completion time and maximizing resource utilization. This paper introduces Cat Swarm Optimization algorithm for task scheduling, showing significant improvements in completion time, energy consumption, and total power cost over existing algorithms when applied to HPC2N and NASA workloads.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Environmental Sciences
Jianli Liu, Xiaohan Liao, Huping Ye, Huanyin Yue, Yong Wang, Xiang Tan, Dongliang Wang
Summary: In this study, a UAV swarm scheduling method is proposed to address the challenges in UAV remote sensing in emergency scenarios. By decomposing tasks and optimizing the flight ranges, the method achieves the shortest total flight range of tasks and balances the flight ranges of each UAV.
Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Woraphrut Kornmaneesang, Shyh-Leh Chen
Summary: This paper proposes a time-optimal feedrate scheduling approach for toolpaths in 5-axis machining. The method describes the toolpath in the spherical coordinate system, utilizes a quintic B-spline corner smoothing method, and optimizes the feedrate profile using particle swarm optimization. Experimental results show that the proposed method achieves shorter cycle time and less contour errors.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Engineering, Mechanical
Xiuli Chai, Jiangyu Fu, Zhihua Gan, Yang Lu, Yushu Zhang
Summary: This paper proposes an effective visually meaningful color image encryption scheme by combining HMPSO, BCS and HD algorithms. The scheme uses techniques such as discrete cosine transform, chaotic sequence generation, and embedding algorithm to enhance the security and efficiency of image encryption. By optimizing the threshold value of sparse coefficient modification and the embedding rate, the quality of the reconstructed image and cipher image is improved.
NONLINEAR DYNAMICS
(2022)
Article
Green & Sustainable Science & Technology
Y. Supriya, Thippa Reddy Gadekallu
Summary: Forests are crucial for the ecological system, but forest fires pose a serious threat. Artificial intelligence techniques such as machine learning and deep learning have been used to predict forest fires and address challenges. This paper proposes a federated learning framework with a particle swarm optimization algorithm, which achieves a prediction accuracy of 94.47% using multidimensional forest fire image data, and can be an essential component in the development of early warning systems for forest fires.
Article
Mechanics
Parviz Mohammad Zadeh, Mostafa Mohagheghi
Summary: This paper presents an efficient multi-objective reliability-based design optimization method for composite structures, utilizing a hybrid decomposing-based algorithm and bi-level modeling strategy with both reliability and weight as objective functions. Demonstrated using laminated composite plate and benchmark problems, the proposed method shows computational efficiency and accuracy in evaluating the design performance of composite structures.
COMPOSITE STRUCTURES
(2022)
Article
Computer Science, Theory & Methods
Bo Huang, Mengchu Zhou, Xiaoyu Sean Lu, Abdullah Abusorrah
Summary: Resource allocation systems (RASs) are commonly used discrete event systems in the industry, where available resources are allocated to optimize performance criteria. This paper provides a tutorial and comprehensive literature survey on RG-based RSP methods, presenting a framework for RSPs and their PNs, construction methods, scheduling objectives, search strategies, heuristic functions, and future research directions.
ACM COMPUTING SURVEYS
(2023)
Article
Computer Science, Artificial Intelligence
Fatemeh Mohammadi Shakiba, S. Mohsen Azizi, Mengchu Zhou, Abdullah Abusorrah
Summary: This paper surveys recent machine learning-based techniques for fault detection, classification, and location estimation in transmission lines. It emphasizes the need for faster and more accurate fault identification tools and introduces various machine learning methodologies and artificial neural networks used in diagnosing transmission line faults.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Engineering, Electrical & Electronic
Hongyu Xie, Dong Zhang, Jun Wang, MengChu Zhou, Zhengcai Cao, Xiaobo Hu, Abdullah Abusorrah
Summary: This research proposes a semidirect multimap monocular SLAM system (SM-SLAM) that combines direct tracking and feature-based map maintenance with point features and line segments. The experimental results show that SM-SLAM can accurately reconstruct a sparse 3D map with geometrical structure information in low-textured environments at a speed of 30-40 Hz.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
ShouGuang Wang, Xin Guo, Oussama Karoui, MengChu Zhou, Dan You, Abdullah Abusorrah
Summary: This study focuses on the deadlock control problem in resource allocation systems using mixed-integer programming and iterative siphon control. It proposes a two-stage deadlock prevention policy, which avoids exhaustive enumeration and reachability analysis.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
SiYa Yao, Qi Kang, MengChu Zhou, Muhyaddin J. Rawa, Aiiad Albeshri
Summary: This article proposes an efficient discriminative manifold distribution alignment (DMDA) approach, which improves feature transferability by aligning both global and local distributions and refines a discriminative model by learning geometrical structures in manifold space. Extensive experiments show that DMDA outperforms other methods in both classification accuracy and time efficiency in domain adaptation tasks.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Xiaoling Wang, Qi Kang, Mengchu Zhou, Zheng Fan, Aiiad Albeshri
Summary: Multi-task optimization (MTO) is a new evolutionary computation paradigm that solves multiple optimization tasks concurrently by utilizing task similarities and historical knowledge. This work proposes the individually guided multi-task optimization (IMTO) framework, which explores each individual to learn from other tasks, selects individuals with higher solving ability, and only inferior individuals learn from other tasks to improve knowledge transfer. The advantage of IMTO over multifactorial evolutionary framework and baseline solvers is verified through benchmark studies.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Yanlu Gong, Quanwang Wu, Mengchu Zhou, Junhao Wen
Summary: Multi-label learning aims to solve classification problems where instances are associated with a set of labels. This work proposes a novel approach called Self-paced Multi-label Co-Training (SMCT) that leverages the co-training paradigm to train two classifiers iteratively and communicate predictions on unlabeled data. Experimental evaluations demonstrate the competitive performance of SMCT compared to state-of-the-art methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Zizhen Zhang, Hong Liu, MengChu Zhou, Jiahai Wang
Summary: This article introduces the traveling salesman problem (TSP) and its dynamic versions (DTSP and DPDP). By improving the attention model, a deep reinforcement learning algorithm is proposed to solve DTSP and DPDP problems. Experimental results show that this method can capture dynamic changes and produce satisfactory solutions in a short time, with over 5% improvements observed in many cases compared to other baseline approaches.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shangce Gao, MengChu Zhou, Ziqian Wang, Daiki Sugiyama, Jiujun Cheng, Jiahai Wang, Yuki Todo
Summary: This article introduces a single dendritic neuron model (DNM) with nonlinear information processing ability, which is extended to complex-valued domain. Experimental results demonstrate that this complex-valued DNM significantly outperforms real-valued DNM, complex-valued multi-layer perceptron, and other complex-valued neuron models.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Guiyuan Yuan, Jiujun Cheng, MengChu Zhou, Sheng Cheng, Shangce Gao, Changjun Jiang, Abdullah Abusorrah
Summary: Accurately processing dynamic evolution events is extremely challenging for autonomous vehicle groups in an urban scene, as they are affected by manned vehicles, roadside obstacles, traffic lights, and pedestrians. Existing work in this area has focused on highway scenes and cannot be directly applied to urban scenes due to different environmental factors and incomplete dynamic evolution events. In this study, a dynamic evolution method specifically designed for autonomous vehicle groups in urban scenes is proposed, which analyzes the reasons for dynamic evolution, abstracts five dynamic evolution events, and introduces a method to process them. Simulation results demonstrate that the proposed method outperforms the existing highway scene method in terms of connectivity, coupling, timeliness, and evolvability of vehicle groups.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Li Huang, MengChu Zhou, Kuangrong Hao, Hua Han
Summary: This paper proposes a distributed event-driven cooperative strategy for multirobot systems to autonomously patrol moving objects. It defines forward and backward utility functions as evaluation criteria for robots to choose patrol targets, and proposes three event types and a cooperative action considering energy consumption and visiting frequency to improve coordination among robots in their execution processes. Simulation experiments show that the proposed strategy has significant advantages in decreasing the average and maximum unvisited time of moving objects compared to the state-of-the-art. A marine pollution monitoring case is simulated to demonstrate the practicability of this strategy.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Huan Liu, Junqi Zhang, MengChu Zhou
Summary: This paper proposes an adaptive particle swarm optimizer that combines hierarchical learning with variable population to enhance the performance of the PSO algorithm. By introducing a heap-based hierarchy and adjusting the particle's level based on its current fitness, as well as eliminating redundant particles based on the population's evolution state, the swarm's exploratory and exploitative capabilities are improved.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Weiling Li, Renfang Wang, Xin Luo, MengChu Zhou
Summary: This study proposes a second-order symmetric non-negative latent factor model (SNLF)-N-2 with an efficient second-order learning algorithm for precise representation of undirected weighted networks. The model applies a single latent factor-related mapping function to achieve an unconstrained learning objective and optimizes this objective with a second-order learning algorithm. Empirical studies show that the proposed model outperforms existing second-order SNLF models in accurately representing real-world undirected weighted networks.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Cybernetics
Leifeng He, Guanjun Liu, Mengchu Zhou
Summary: This article presents a method to address privacy issues in multiagent systems using reduced ordered binary decision diagrams (ROBDD). By designing related algorithms and a model checking tool, complex CTLK formulas can be efficiently verified.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Computer Science, Cybernetics
Yu Xie, Guanjun Liu, Chungang Yan, Changjun Jiang, MengChu Zhou
Summary: This study proposes a new model to extract transactional behaviors of credit card users and learn new transactional behavioral representations for fraud detection. The model utilizes time-aware gates and an attention module to capture long- and short-term transactional habits of users and extract behavioral motive and periodicity from historical transactions.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
