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Computer Science, Information Systems
Tianping Liu, Guojiang Xiong, Ali Wagdy Mohamed, Ponnuthurai Nagaratnam Suganthan
Summary: The paper proposes an improved differential evolution (DE) algorithm OMLIDE based on opposition-mutual learning, hybrid mutation strategy, and parameters adaptive mechanism, effectively solving the economic load dispatch (ELD) problem in power systems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Derong Lv, Guojiang Xiong, Xiaofan Fu
Summary: A MOEED model considering the uncertainty of solar power is proposed in this study, and it is effectively solved using an extended quadratic interpolation learning differential evolution method. The model takes into account both underestimation and overestimation of solar power, penalizing them in terms of operating cost. Additionally, several multi-objective processing techniques are integrated, including feasible solution technique, non-dominated sorting, summation-based sorting, diversified selection method, and fuzzy decision technique. Simulation results validate the superiority of the proposed method.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Thermodynamics
Wenqiang Yang, Xinxin Zhu, Qinge Xiao, Zhile Yang
Summary: This paper proposes an improved version of the multi-objective marine predator algorithm (IMOMPA) for solving the optimization of multi-objective dynamic economic-grid fluctuation dispatch (MODEGD). The IMOMPA algorithm improves population diversity, convergence speed, and global search ability. Numerical experiments on benchmark functions and generation units demonstrate the superiority of the IMOMPA algorithm, and plug-in electric vehicles (PEVs) connected to the grid (V2G) can help mitigate grid fluctuations.
Article
Computer Science, Artificial Intelligence
Liyun Fu, Haibin Ouyang, Chengyun Zhang, Steven Li, Ali Wagdy Mohamed
Summary: This paper proposes a constrained cooperative adaptive multi-population differential evolutionary (CCAM-PDE) algorithm for solving large-scale and complex constrained engineering optimization problems. The algorithm enhances the global search capability by introducing a dynamic constraint handling region and an improved population generation scheme, as well as improves the differential evolution algorithm through constraint handling technology. Experimental results demonstrate that the CCAM-PDE algorithm performs well in constraint handling efficiency, global search capability, and convergence speed.
APPLIED SOFT COMPUTING
(2022)
Article
Green & Sustainable Science & Technology
Bishwajit Dey, Fausto Pedro Garcia Marquez, Aniruddha Bhattacharya
Summary: This research uses a hybrid intelligence method to reduce the total cost of microgrid systems and incorporates demand-side management to optimize the demand model and adjust the timing of elastic loads, resulting in energy cost savings.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Thermodynamics
Qiang Zhang, Dexuan Zou, Na Duan
Summary: With the increasing demand for electric energy and worsening environmental pollution globally, economic emission dispatch (EED) has become one of the most crucial problems in power systems. The introduction of plug-in electric vehicles (PEVs) as a popular transportation option has the potential to improve quality of life and reduce emissions even further. However, the frequent and unpredictable charging of PEVs poses a risk to the stability of the power grid. Therefore, the dynamic economic emission dispatch considering plug-in electric vehicles (DEED-PEV) has gained significant importance in power systems.
Article
Mathematics
Mokhtar Said, Ali M. El-Rifaie, Mohamed A. Tolba, Essam H. Houssein, Sanchari Deb
Summary: Economic Load Dispatch is a critical issue in power engineering, aiming to minimize production costs and reduce emissions. The Chameleon Swarm Algorithm showed promising performance in addressing this problem.
Article
Thermodynamics
Xu Chen, Guowei Tang
Summary: An improved competitive swarm optimization (ImCSO) algorithm is proposed in this paper to solve Multi-area Economic Dispatch (MAED) problems, which enhances performance by introducing a ranking paired learning strategy and a differential evolution strategy. Experimental results show that the ImCSO algorithm has superior solution accuracy and reliability in solving MAED problems.
Article
Engineering, Electrical & Electronic
Homayoun Berahmandpour, Shahram Montaser Kouhsari, Hassan Rastegar
Summary: This paper introduces a new flexibility-based risk limiting dynamic economic load dispatch solution that incorporates wind power. The authors discuss how to reduce the risk of load shedding and wind power curtailment through economic trade-offs between system flexibility and these unwanted risks.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Qinghua Liu, Guojiang Xiong, Xiaofan Fu, Ali Wagdy Mohamed, Jing Zhang, Mohammed Azmi Al-Betar, Hao Chen, Jun Chen, Sheng Xu
Summary: This study proposes a new hybrid method, GSK-DE, to solve large-scale ED problems by integrating the advantages of GSK and DE algorithms. By dividing the population into two subpopulations, one performing GSK and the other executing DE, and combining the updated individuals, GSK-DE improves the searching efficiency. Simulation results demonstrate that GSK-DE achieves quicker global convergence, higher quality dispatch schemes, and greater robustness.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Di Liu, Zhongbo Hu, Qinghua Su, Mianfang Liu
Summary: The paper introduces a niching differential evolution algorithm (NDE) to solve the large-scale CHPED problem. By incorporating niching methods into the differential evolution algorithm and implementing a two-phase selection strategy, the NDE balances global and local search capabilities effectively.
APPLIED SOFT COMPUTING
(2021)
Article
Energy & Fuels
Abdulaziz Almalaq, Tawfik Guesmi, Saleh Albadran
Summary: This study proposes a new multiobjective optimization technique combining the differential evolution algorithm and chaos theory to solve the nonconvex and nonsmooth economic emission dispatch problem. The technique extracts an accurate Pareto front and overcomes the limitations of local optima and the conventional DE algorithm. A slack TGU is defined to handle the power balance constraint, and the proposed technique optimizes the thermal units to achieve the optimization objectives.
Article
Computer Science, Artificial Intelligence
Baihao Qiao, Jing Liu, Xingxing Hao
Summary: This paper introduces a new method to solve the dynamic economic emission dispatch (DEED) problems, which includes a PDAD variable method and a DSL method, and applies the NSDESa_LS algorithm. Experimental results demonstrate that the proposed method performs superiorly on 5, 10, and 40-unit systems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Di Liu, Zhongbo Hu, Qinghua Su
Summary: This study proposes a neighborhood-based differential evolution algorithm with direction induced strategy (NDIDE) to solve the large-scale CHPED problem. The algorithm demonstrates good feasibility and superior performance in several CHPED systems.
INFORMATION SCIENCES
(2022)
Article
Thermodynamics
Dexuan Zou, Dunwei Gong
Summary: The new differential evolution algorithm improves performance in solving the combined heat and power dynamic economic dispatch problem by introducing an attracting factor and a method of repairing solutions.
Article
Engineering, Electrical & Electronic
M. Taghizadeh, S. Sadughi
ELECTRONICS LETTERS
(2015)
Article
Automation & Control Systems
Mojtaba Ghasemi, Mandi Taghizadeh, Sahand Ghavidel, Jamshid Aghaei, Abbas Abbasian
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2015)
Article
Engineering, Electrical & Electronic
Mahdi Taghizadeh, Mohsen Maddahali
IET MICROWAVES ANTENNAS & PROPAGATION
(2018)
Article
Green & Sustainable Science & Technology
Mahdi Taghizadeh, Mohammad Mardaneh, Mokhtar Sha Sadeghi
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2014)
Article
Green & Sustainable Science & Technology
Mahdi Taghizadeh, Mohammad Mardaneh, Mokhtar Sha Sadeghi
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2014)
Article
Engineering, Electrical & Electronic
Mahdi Taghizadeh, Mohammad Hoseintabar, Jawad Faiz
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2015)
Article
Engineering, Electrical & Electronic
Alidoost Rostamizadeh, Mehdi Taghizadeh, Jasem Jamali, Alireza Andalib
OPTICAL AND QUANTUM ELECTRONICS
(2020)
Article
Chemistry, Physical
Azam Beigie Kheradmand, Morteza Tayebi, Mohamad Mehdi Akbari, Abbas Abbasian
Summary: The microstructural changes and mechanical properties of Al-Zn-Mg-Cu-Zr alloy samples with the addition of Sc were investigated. The results showed that the addition of Sc resulted in grain-refined microstructures and smooth precipitates. Furthermore, the aging treatment after hot rolling process increased the precipitate content, leading to improved mechanical properties.
JOURNAL OF ALLOYS AND COMPOUNDS
(2022)
Article
Physics, Condensed Matter
Sohrab Mohammadi, Mehdi Taghizadeh, Hassan Masoumi
Summary: This work presents the spin transport properties of a (10, 0) boron nitride nanotube (BNNT), where transitional metals (Fe, Ni, and Co) are doped to induce high spin polarization. The calculations show that doping Ni at the N site results in a pure spin down current, while doping Fe at the N site leads to a pure spin up current. All dopants create pure spin-polarized levels within the band gap. Doping transition metal on the B sites induces a higher magnetic dipole moment compared to the N sites.
COMPUTATIONAL CONDENSED MATTER
(2022)
Article
Engineering, Electrical & Electronic
Farshad Ghaedi, Jasem Jamali, Mehdi Taghizadeh
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
(2020)
Article
Computer Science, Artificial Intelligence
Elham Ganji, Reyhaneh Kardehi Moghaddam, Ali Toloui, Mahdi Taghizadeh
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2014)
Article
Computer Science, Artificial Intelligence
Elham Ganji, Reyhaneh Kardehi Moghaddam, Ali Toloui, Mahdi Taghizadeh
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2014)
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)
