Article
Computer Science, Artificial Intelligence
Shih-Wei Lin, Chen-Yang Cheng, Pourya Pourhejazy, Kuo-Ching Ying
Summary: Scheduling problems are crucial in modern manufacturing, and an improved meta-heuristic algorithm, MTSA, has been proposed for Permutation Flowshop Scheduling Problem with Mixed-Blocking Constraints, outperforming existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Management
Luc Libralesso, Pablo Andres Focke, Aurelien Secardin, Vincent Jost
Summary: We examine an iterative beam search algorithm for the permutation flowshop problem, which combines branching strategies from recent branch-and-bounds algorithms and a guidance strategy inspired by the LR heuristic. The algorithm achieves competitive results on large instances and reports numerous new-best-so-far solutions on the VFR and Taillard benchmarks without using NEH-based branching or iterative-greedy strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Engineering, Industrial
Biao Han, Quan-Ke Pan, Liang Gao
Summary: This paper addresses a serial distributed permutation flowshop scheduling problem (SDPFSP) inspired by a printed circuit board assembly process. A cooperative iterated greedy (CIG) algorithm is developed to optimize the solution. Problem-specific operators and computational experiments are conducted to verify the effectiveness of the proposed algorithm and its superiority over existing methods.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Social Sciences, Interdisciplinary
Iqbal Hayat, Adnan Tariq, Waseem Shahzad, Manzar Masud, Shahzad Ahmed, Muhammad Umair Ali, Amad Zafar
Summary: Permutation flow-shop scheduling is a strategy that optimizes the processing of jobs while ensuring the same order on subsequent machines. Particle Swarm Optimization (PSO) has been frequently used for this purpose. This research developed a standard PSO and hybridized it with Variable Neighborhood Search (PSO-VNS) and Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The hybrid PSO (HPSO) performed well compared to other algorithms, with an ARPD value of 0.48 indicating robustness and improved performance in optimizing makespan.
Article
Operations Research & Management Science
Wahiba Jomaa, Mansour Eddaly, Bassem Jarboui
Summary: This paper proposes two variable neighborhood search algorithms to solve the permutation flowshop scheduling problem considering preventive maintenance in the non-resumable case. Computational results demonstrate the high performance of these algorithms compared to other approaches, and it is suggested that the change of initial solutions during the optimization process may improve algorithm performance.
OPERATIONAL RESEARCH
(2021)
Article
Automation & Control Systems
Yuan-Zhen Li, Kaizhou Gao, Lei-Lei Meng, Ponnuthurai Nagaratnam Suganthan
Summary: This work addresses the distributed permutation flowshop scheduling problem (DPFSP) with peak power consumption. An improved artificial bee colony (IABC) algorithm is proposed to solve the problem, utilizing new solution generation operators and a local search operation. Experimental results show that the IABC algorithm performs well in solving the DPFSP with peak power consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Heba M. Eldesokey, Saied M. Abd El-atty, Walid El-Shafai, Mohammed Amoon, Fathi E. Abd El-Samie
Summary: Cloud computing is the current standard for computing, providing IT services over the Internet on demand. Task scheduling is crucial in cloud environments, with the proposed HSO algorithm combining PSO and SSO to optimize efficiency. Additionally, MLR is used to detect overloaded VMs and improve resource utilization and reduce computational costs.
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
(2021)
Article
Mathematics
Chenyao Zhang, Yuyan Han, Yuting Wang, Junqing Li, Kaizhou Gao
Summary: A distributed blocking flowshop scheduling problem with no buffer and setup time constraints is studied. A mixed integer linear programming model is constructed and verified for correctness. An iterated greedy algorithm is presented to optimize the makespan criterion and collaborative interactions are considered to improve the exploration and exploitation of the algorithm.
Article
Computer Science, Artificial Intelligence
Danyu Bai, Xiaoyuan Bai, Haoran Li, Quan-ke Pan, Chin-Chia Wu, Liang Gao, Meiting Guo, Lin Lin
Summary: This study addresses the blocking flowshop scheduling problems and proposes exact and metaheuristic algorithms to optimize efficiency by considering different criteria such as makespan, maximum lateness, or maximum delivery-completion time. The algorithms are designed for different scale instances and have been evaluated through comprehensive computational tests.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Muhammad Usman Sana, Zhanli Li, Fawad Javaid, Muhammad Wahab Hanif, Imran Ashraf
Summary: This study proposes a novel encoding technique using blockchain and Improved Particle Swarm Optimization (IPSO) to improve the makespan value and scheduling time. The experimental results indicate that the proposed algorithm is practical and secure in handling flexible job scheduling and outperforms the state-of-the-art task scheduling algorithms.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Interdisciplinary Applications
Young In Cho, So Hyun Nam, Ki Young Cho, Hee Chang Yoon, Jong Hun Woo
Summary: During the shipbuilding process, optimizing the scheduling of a block assembly line is crucial for productivity. This study proposes using a reinforcement learning algorithm based on a pointer network to improve the control of inbound product sequence. The trained model shows promising results compared to heuristic and metaheuristic algorithms in terms of makespan and computation time.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Jia-yang Mao, Quan-ke Pan, Zhong-hua Miao, Liang Gao
Summary: This paper studies the distributed permutation flowshop scheduling problem with preventive maintenance operation, proposing a multi-start iterated greedy algorithm to minimize makespan. By improving heuristic methods and introducing destruction and construction phases, it avoids local optima and strengthens the exploitation of local neighborhood solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Quan-Ke Pan, Liang Gao, Ling Wang
Summary: This article addresses a novel scheduling problem in modern manufacturing systems and proposes a cooperative co-evolutionary algorithm to solve it. Experimental results show that the algorithm outperforms other metaheuristics in the literature.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Multidisciplinary Sciences
Zhengyu Hu, Wenrui Liu, Shengchen Ling, Kuan Fan
Summary: The paper presents a method to address the issue of unbalanced workload of employees in parallel flow shop scheduling. By establishing a bi-objective nonlinear integer programming model and designing heuristic rule algorithms for solving, the experimental results demonstrated the advantages of the model and method.
Article
Engineering, Chemical
Kun Li, Huixin Tian
Summary: This paper proposes a learning and swarm based multiobjective variable neighborhood search (LS-MOVNS) algorithm to solve the multiobjective PFSP problem. LS-MOVNS achieves a balance between exploration and exploitation in a multiobjective environment through integrating swarm-based search with VNS using machine learning techniques.
Article
Engineering, Industrial
Peixin Ge, Ying Meng, Jiyin Liu, Lixin Tang, Ren Zhao
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2020)
Article
Engineering, Industrial
Yanyan Zhang, Gary G. Yen, Lixin Tang
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2020)
Article
Engineering, Multidisciplinary
L. J. Tang, X. P. Wang, L. X. Tang, C. Cheng, Y. Yang
ENGINEERING OPTIMIZATION
(2020)
Article
Computer Science, Information Systems
Fei Zou, Gary G. Yen, Lixin Tang
INFORMATION SCIENCES
(2020)
Article
Economics
Defeng Sun, Ying Meng, Lixin Tang, Jinyin Liu, Baobin Huang, Jiefu Yang
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2020)
Article
Computer Science, Information Systems
Zhiming Dong, Xianpeng Wang, Lixin Tang
INFORMATION SCIENCES
(2020)
Article
Metallurgy & Metallurgical Engineering
Yao Wang, Xianpeng Wang, Zhiming Dong, Zan Wang
ISIJ INTERNATIONAL
(2020)
Article
Automation & Control Systems
Xianpeng Wang, Zhiming Dong, Lixin Tang
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2020)
Article
Computer Science, Artificial Intelligence
Linlin Li, Xianpeng Wang
Summary: The paper proposes an adaptive multi-objective evolutionary algorithm AGMOEA, which divides the objective space into subspaces based on the grid system, dynamically allocates evolutionary opportunities to different subspaces based on relationships between them. Experimental results demonstrate the algorithm's strong competitiveness.
Article
Automation & Control Systems
Zhiming Dong, Xianpeng Wang, Lixin Tang
Summary: The production scheduling of color-coated steel coils is crucial for steel enterprises, and in this study, a multiobjective optimization model and a new evolutionary algorithm were proposed to address the conflicting objectives in scheduling, improving production efficiency and economic benefits.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Xianpeng Wang, Tenghui Hu, Lixin Tang
Summary: This article introduces a novel multiobjective evolutionary nonlinear ensemble learning model that improves the accuracy and stability of silicon content prediction models through evolutionary feature selection and nonlinear ensemble strategies. The model outperforms other prediction models and effectively selects critical features.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Automation & Control Systems
Xianpeng Wang, Yao Wang, Lixin Tang
Summary: This article proposes a method called MOSNE-EFS for predicting strip hardness in the steel industry. Experimental results show that the evolutionary feature selection and sparse nonlinear ensemble strategies effectively improve the accuracy and robustness of the prediction model, with the MOSNE-EFS model outperforming other existing methods.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Xianpeng Wang, Zhiming Dong, Lixin Tang, Qingfu Zhang
Summary: This article proposes a multiobjective multitask optimization algorithm based on decomposition with dual neighborhoods, which improves algorithm performance by transferring knowledge among different tasks through neighborhood usage.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Yao Wang, Zhiming Dong, Tenghui Hu, Xianpeng Wang
2020 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2020)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
