Article
Geography, Physical
Rui Li, Bingzhen Chen, Guangsheng Dong, Huayi Wu
Summary: The research proposed a novel method for optimizing the visualization of distribution networks using a force-directed algorithm, a Delaunay triangulation, and a genetic algorithm. A visualization evaluation indicator was introduced for quantitative assessment, and a fisheye algorithm was utilized to further improve the visualization of compact districts. The trade-off between accurate spatial location and topology clarity was balanced by using limited spatial displacement.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2021)
Article
Computer Science, Interdisciplinary Applications
Xiankun Lin, Xianxing Yu, Weidong Li
Summary: A niching hybrid heuristic whale optimization algorithm (NHWOA) is proposed in this research to enhance convergence speed and search coverage in solving global optimization problems. The algorithm addresses the issue of premature convergence in whale optimization algorithm by introducing the niching technique and heuristic adjustment of parameters.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Shuhao Jiang, Jiahui Shang, Jichang Guo, Yong Zhang
Summary: To overcome the limitations of the Flamingo Search Algorithm (FSA) and improve solution accuracy, an improved algorithm called the Multi-Strategy Improved Flamingo Search Algorithm (IFSA) is introduced. IFSA utilizes a cube chaotic mapping strategy for generating initial populations and improves the information feedback model strategy. Furthermore, it introduces Random Opposition Learning and Elite Position Greedy Selection strategies to improve convergence. Experimental results show that IFSA achieves higher convergence accuracy and better exploration abilities, providing a new optimization algorithm for complex problems.
APPLIED SCIENCES-BASEL
(2023)
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, Interdisciplinary Applications
Rong Zheng, Abdelazim G. Hussien, Raneem Qaddoura, Heming Jia, Laith Abualigah, Shuang Wang, Abeer Saber
Summary: The African vultures optimization algorithm (AVOA) is a metaheuristic inspired by the African vultures' behaviors. However, it suffers from slow convergence rate and local optimal stagnation. In this study, an enhanced version called EAVOA is introduced, using techniques such as representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism. EAVOA outperforms other methods in terms of numerical results and convergence curves, and shows practical applicability in engineering design optimization problems and classification tasks.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Nating Du, Yongquan Zhou, Qifang Luo, Ming Jiang, Wu Deng
Summary: This paper proposes MSChimp to address the shortcomings of Chimp optimization algorithm (ChOA), which are falling into local optimal value easily and imbalanced global exploration and local exploitation abilities. The research work mainly focuses on introducing opposition-based learning strategy in the initialization stage to enhance population diversity, and introducing Sine Cosine Algorithm (SCA) in the exploitation process to improve convergence speed and accuracy. Experimental results demonstrate that the improved ChOA significantly enhances the ability to find optimal values, highlighting the effectiveness and feasibility of MSChimp. It also shows strong competitiveness compared to other algorithms.
Article
Computer Science, Artificial Intelligence
Hu Peng, Zhaogan Zeng, Changshou Deng, Zhijian Wu
Summary: Cuckoo search algorithm is effective but can get trapped in local optimum due to unitary search strategy. To overcome this, a multi-strategy serial CS algorithm (MSSCS) is proposed with new learning strategies based on cuckoo's behavior, aiming to enhance performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Civil
Pengcheng Li, Hao Wang
Summary: This study introduces a novel strategy for optimizing the crossarm length of PSSCs based on multi-dimensional global optimization algorithms, using a multi-population genetic algorithm. The proposed optimization algorithm was shown to be efficient and accurate in determining the optimal crossarm length of PSSCs.
ENGINEERING STRUCTURES
(2021)
Article
Multidisciplinary Sciences
Shanshan Xie, Yan Zhang, Danjv Lv, Haifeng Xu, Jiang Liu, Yue Yin
Summary: Birds are environmental indicators that can reflect changes in the ecological environment and biodiversity. This study proposes a multi-strategy differential evolution method to optimize the parameters of the extreme learning machine (ELM) for bird species recognition. Experimental results show that the proposed model achieves higher recognition accuracy and stability compared to other models.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Interdisciplinary Applications
Heming Jia, Yongchao Li, Di Wu, Honghua Rao, Changsheng Wen, Laith Abualigah
Summary: This paper proposes a metaheuristic algorithm called ROA, which simulates the foraging behavior of remora. However, the performance of ROA can still be improved, especially in dealing with complex optimization problems. Inspired by Survival of the fittest, a random restart strategy is introduced to help ROA escape local optimal solutions, and additional strategies based on information entropy evaluation and visual perception are added. The resulting multi-strategy Remora Optimization Algorithm (MSROA) shows strong optimization capabilities and competitiveness in solving practical engineering problems.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Hongliang Zhang, Zhennao Cai, Xiaojia Ye, Mingjing Wang, Fangjun Kuang, Huiling Chen, Chengye Li, Yuping Li
Summary: This paper presents an enhanced salp swarm algorithm (ESSA) by incorporating strategies such as orthogonal learning, quadratic interpolation, and generalized oppositional learning. Experimental results demonstrate that ESSA significantly outperforms the original salp swarm algorithm and other state-of-the-art methods.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Artificial Intelligence
Wenyi Long, Huachao Dong, Peng Wang, Yan Huang, Jinglu Li, Xubo Yang, Chongbo Fu
Summary: This paper proposes an efficient global diversity CMOEA (EGDCMO) to solve constrained multi-objective optimization problems (CMOPs), which addresses the issues of small feasible regions and complex constraints by maintaining a certain number of well-distributed infeasible solutions in the evolutionary process.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Mathematics, Interdisciplinary Applications
Jinwei Pang, Xiaohui Li, Shuang Han
Summary: Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. The algorithm utilizes an adaptive selection strategy to adjust the probability of selecting different variants based on the rate of fitness value change. To enhance the algorithm's exploitation ability, a Nelder-Mead variant method is also introduced.
Article
Engineering, Multidisciplinary
Lingyun Deng, Sanyang Liu
Summary: This study introduces a multi-strategy improved slime mould algorithm (MSMA) to balance exploitation and exploration. The algorithm enhances search efficiency through a new search equation, dynamic random search technique, and adaptive mutation probability. Evaluation on benchmark functions and practical engineering issues demonstrates that MSMA is more efficient and robust than other state-of-the-art techniques.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Mingyuan Li, Xiaobing Yu, Bingbing Fu, Xuming Wang
Summary: This article proposes a Modified Whale Optimization Algorithm (MWOA) with multi-strategy mechanism to improve the efficiency of WOA by introducing elite reverse learning strategy, nonlinear convergence factor, DE/rand/1 mutation strategy, and Levy flight disturbance strategy. MWOA has strong competitiveness and can better improve the efficiency of WOA according to the experimental results and analysis.
NEURAL COMPUTING & APPLICATIONS
(2023)
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)