Article
Engineering, Aerospace
Ping Lu
Summary: A novel approach is proposed in this paper to handle nonlinear equality constraints through convex-concave decomposition, theoretically establishing the same solution as the original problem. The application in a fuel-optimal finite-thrust spacecraft circumnavigation problem demonstrates the effectiveness of the approach over the conventional linearization method in most cases.
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2021)
Article
Computer Science, Information Systems
Laith Abualigah, Akram Jamal Dulaimi
Summary: The SCAGA method combines the SCA and GA algorithms, demonstrating better performance in balancing the exploitation and exploration strategies of the search space.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Robotics
Philipp Foehn, Angel Romero, Davide Scaramuzza
Summary: Quadrotors are known for their agility, but planning time-optimal trajectories through multiple waypoints has been a challenge. This study introduces a new method that simultaneously optimizes time allocation and trajectory to generate truly time-optimal trajectories, surpassing human expert drone pilots in a drone-racing task.
Article
Computer Science, Artificial Intelligence
Arun Kumar, Kamlesh Dutta, Abhishek Srivastava
Summary: This paper discusses the process of layout planning in a house or building, which involves placing layout entities, such as kitchen, living-room, office-space, in appropriate positions that conform to topological and dimensional constraints. Various approaches are utilized, including the ancient Indian system of architecture known as Vaastu Shastra, to optimize the layout design. The system implements a Multi-Population Genetic Algorithm (MPGA) to determine the topological relations between layout entities and an Entity Planning Genetic Algorithm (EPGA) to optimally place the groups in the layout, while preserving dimensional constraints.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Wei Kaidi, Mohammad Khishe, Mokhtar Mohammadi
Summary: This study introduces the Dynamic Levy Flight technique to enhance the Chimp Optimization Algorithm, achieving good results in various standard and challenging functions as well as practical optimization problems. DLFChOA and CMA-ES perform well in most numerical test functions, and achieve the best results in some real-world engineering problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Shubham Mahajan, Laith Abualigah, Amit Kant Pandit, Mohammad Rustom Al Nasar, Hamzah Ali Alkhazaleh, Maryam Altalhi
Summary: The paper proposes a fusion method for global optimization tasks that can be applied to different problems. Through thorough testing and analysis, it is shown to have efficient performance.
Article
Computer Science, Information Systems
Haihua Gu, Xiaoping Li, Zhipeng Lu
Summary: This paper investigates Spark task scheduling with data skew and deadline constraints, and proposes an optimized algorithm which outperforms existing algorithms in big data processing performance based on experimental results.
Article
Computer Science, Artificial Intelligence
Marwa F. F. Mohamed, Mai Dahshan, Kenli Li, Ahmad Salah
Summary: VM replication is a critical task in cloud computing platforms to ensure service availability. This study proposes an optimal VM placement (VMP) method considering VM replication requirements, using a multi-objective sorting genetic algorithm (NSGA-III) to address the problem. The results show that NSGA-III outperforms other comparison methods, including heuristic and meta-heuristic approaches, in terms of performance.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Maroua Ghali, Sami Elghali, Nizar Aifaoui
Summary: This paper aims to establish a tolerance optimization method based on manufacturing difficulty computation using genetic algorithm. The proposed method combines difficulty coefficient computation and genetic algorithm optimization to minimize total cost and meet functional requirements. The results show the advantages of this optimization method compared to other methods through the application on mechanical assemblies.
JOURNAL OF INTELLIGENT MANUFACTURING
(2023)
Article
Multidisciplinary Sciences
Lei Wu, Jiawei Wu, Tengbin Wang
Summary: This paper presents an improved grasshopper optimization algorithm called LFGOA, which enhances the global search ability and precision by incorporating the Levy Flight mechanism. Experimental results demonstrate the efficiency of LFGOA and highlight its potential as an alternative solution for meta-heuristic optimization problems with its high exploration and exploitation capabilities.
SCIENTIFIC REPORTS
(2023)
Article
Transportation Science & Technology
Landon C. Willey, John L. Salmon
Summary: The study focuses on Urban Air Mobility (UAM) and the selection of vertiport locations, developing five heuristic algorithms to find potential solutions with acceptable computation times. By comparing results in different regions of the United States, it is shown that the methods are effective in finding solutions within 10% of the optimal solution on average and are scalable to larger networks.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Information Systems
Ahmed A. Rosas, Mona Shokair, M. Dessouky
Summary: This paper studies the joint consideration of power and channel allocation based on genetic algorithm for D2D underlaied cellular networks. The proposed approach demonstrates advantages in maximizing overall system utilization compared to other allocation schemes.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Saso Karakatic
Summary: With the increasing use of electric vehicles in the service industry, optimizing their specific constraints has become more important. This paper presents a Two-Layer Genetic Algorithm for solving the capacitated Multi-Depot Vehicle Routing Problem with Time Windows and Electric Vehicles with partial nonlinear recharging times, aiming to minimize driving times, number of stops at charging stations, and recharging time.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Hala A. Omar, M. A. El-Shorbagy
Summary: Grasshopper optimization algorithm (GOA) is a promising optimization algorithm, but it suffers from the drawback of trapping into local minimum. This paper presents a modified GOA-based genetic algorithm that overcomes this problem by modifying the control parameter and its range. The proposed approach shows significant improvement in convergence rate and search efficiency.
Article
Automation & Control Systems
Mateus de Freitas Virgilio Pereira, Ilya V. Kolmanovsky, Carlos E. S. Cesnik
Summary: This paper presents a constraint aggregation approach for Nonlinear Model Predictive Control (NMPC) by approximating the feasible region with a reduced number of nonlinear constraints. The effect of aggregation on closed-loop system performance and stability is studied, and numerical results for a flexible aircraft model demonstrate significant computational savings and the applicability of the proposed method to large-scale systems.
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)