Article
Computer Science, Artificial Intelligence
Bimal Kumar Dora, Abhishek Rajan, Sourav Mallick, Sudip Halder
Summary: This paper introduces an enhanced Butterfly Optimization Algorithm (EBOA) to solve the Optimal Reactive Power Dispatch (ORPD) problem. The algorithm is evaluated using various test systems and benchmark problems, and the results demonstrate its efficiency and robustness in reducing power loss, voltage deviation, and improving voltage stability.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yasir Muhammad, Muhammad Asif Zahoor Raja, Muhammad Altaf, Farman Ullah, Naveed Ishtiaq Chaudhary, Chi-Min Shu
Summary: This paper presents a new computing paradigm based on fractional order comprehensive learning particle swarm optimization for solving the reactive power dispatch problems in power systems. The method is tested and verified to be stable, effective, and reliable.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Souhil Mouassa, Francisco Jurado, Tarek Bouktir, Muhammad Asif Zahoor Raja
Summary: The proposed nature-inspired optimization algorithm based on artificial ecosystem optimization successfully addresses the ORPD problem in large-scale power systems, achieving better performance compared to existing optimization techniques. This algorithm considers active power losses minimization, voltage deviation, and voltage stability index as three objectives, demonstrating its effectiveness in improving power system performance.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Hamza Yapici
Summary: The study proposes a modified pathfinder algorithm to minimize power losses in solving the ORPD problem. Numerical analyses and statistical tests show that the modified algorithm achieved competitive results and ranking, making it a superior algorithm for solving the ORPD problem.
ENGINEERING OPTIMIZATION
(2021)
Article
Energy & Fuels
Yuanye Wei, Yongquan Zhou, Qifang Luo, Wu Deng
Summary: The paper introduces an improved slime mould algorithm (ISMA) to solve the optimal reactive power dispatch (ORPD) problem, and the performance evaluation and experimental results show that ISMA outperforms in accuracy and computational efficiency.
Article
Thermodynamics
Akanksha Sharma, Sanjay K. Jain
Summary: This paper investigates a day-ahead reactive power ancillary service procurement problem to minimize cost and voltage deviation under wind power generation uncertainties in a pool -based deregulated system. A developed Pareto-based multi-objective artificial electric field algorithm is proposed to solve the problem effectively, validated through comparisons with other algorithms on different test systems. The algorithm utilizes advanced optimization techniques and is analyzed for convergence characteristics and performance under various scenarios.
Article
Chemistry, Multidisciplinary
Le Chi Kien, Chiem Trong Hien, Thang Trung Nguyen
Summary: An improved coyote optimization algorithm (ICOA) was developed for optimal reactive power dispatch (ORPD) problems in transmission power networks, outperforming the conventional COA method. By reducing control parameters and computation steps, ICOA showed better performance and shorter execution time, providing higher solution quality compared to other metaheuristic algorithms.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Salah K. Elsayed, Salah Kamel, Ali Selim, Mahrous Ahmed
Summary: Optimal reactive power dispatch (ORPD) in a typical power system is a complicated multi-objective optimization problem. An Improved Heap-based optimizer (IHBO) is proposed in this paper to enhance the performance of solving ORPD problems. By using chaotic sequences, the performance of HBO is improved to avoid getting stuck in local optima. The effectiveness and robustness of IHBO in solving ORPD problems are demonstrated on three test systems.
Article
Computer Science, Interdisciplinary Applications
Jian Liu, Rui Bo, Siyuan Wang, Haotian Chen
Summary: This paper analyzes how the market impact of large-scale energy storage merchants affects their profits, using dynamic programming theory to study optimal economic dispatch decisions accounting for market impact and storage system physical characteristics. Findings show that State-of-Charge based analytical solutions facilitate merchant decision-making and highlight the need for merchants to balance market impact intensity and dispatched power to maximize profit. Numerical simulations confirm the importance of considering market impact in energy arbitrage decisions for electricity merchants.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Noor Habib Khan, Raheela Jamal, Mohamed Ebeed, Salah Kamel, Hamed Zeinoddini-Meymand, Hossam M. Zawbaa
Summary: This paper aims to solve the optimal reactive power dispatch (ORPD) problem under deterministic and probabilistic states of the system using an improved marine predator algorithm (IMPA). The IMPA enhances the exploitation phase of the algorithm and updates the locations of populations based on spiral orientation and adaptive steps. The proposed algorithm is validated and tested on the IEEE 30-bus system, showing superiority over other state-of-the-art algorithms.
AIN SHAMS ENGINEERING JOURNAL
(2022)
Article
Chemistry, Multidisciplinary
Jairo A. Moran-Burgos, Juan E. Sierra-Aguilar, Walter M. Villa-Acevedo, Jesus M. Lopez-Lezama
Summary: This paper presents a novel multi-period approach for the optimal reactive power dispatch problem, which considers three operative goals and is formulated in GAMS software. The results demonstrate the effectiveness of the proposed approach in reducing operational risks and extending equipment lifespan.
APPLIED SCIENCES-BASEL
(2021)
Article
Green & Sustainable Science & Technology
Salah K. ElSayed, Ehab E. Elattar
Summary: This paper presents the application of the slime mold algorithm for solving the optimal reactive power dispatch problem with renewable energy sources, aiming to minimize system operating costs and improve voltage stability.
Article
Chemistry, Physical
Habtemariam Aberie Kefale, Elias Mandefro Getie, Kassaye Gizaw Eshetie
Summary: This study focuses on minimizing power loss and improving voltage in Ethiopia's radial distribution network, using Selective Particle Swarm Optimization to determine the size and location of solar power installations for enhanced network performance.
INTERNATIONAL JOURNAL OF PHOTOENERGY
(2021)
Article
Computer Science, Information Systems
Noor Habib Khan, Yong Wang, De Tian, Raheela Jamal, Salah Kamel, Mohamed Ebeed
Summary: The FACTS technology plays a crucial role in enhancing system performance, with SSSC being an important member that improves system efficiency by controlling active and reactive power flow in transmission lines. Through the development of efficient optimization algorithms and modifications to the SSSA algorithm, this study successfully addresses the ORPD problem and identifies optimal SSSC configurations, ultimately achieving the goals of enhancing power system stability and reducing losses.
Article
Thermodynamics
Hany M. Hasanien, Ibrahim Alsaleh, Marcos Tostado-Veliz, Miao Zhang, Ayoob Alateeq, Francisco Jurado, Abdullah Alassaf
Summary: This research introduces a novel technique, the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm, for solving the optimal reactive power dispatch (ORPD) problem in electrical grids. Simulation studies verify its efficacy and real data on electric vehicles are incorporated for realistic analyses.
Article
Engineering, Electrical & Electronic
Abhishek Rajan, T. Malakar
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2015)
Article
Engineering, Electrical & Electronic
T. Malakar, Abhishek Rajan, K. Jeevan, Pinaki Dhar
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2016)
Article
Engineering, Electrical & Electronic
Abhishek Rajan, T. Malakar
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2016)
Article
Computer Science, Artificial Intelligence
Ram Kumar, Abhishek Rajan, Fazal A. Talukdar, Nilanjan Dey, V. Santhi, Valentina E. Balas
NEURAL COMPUTING & APPLICATIONS
(2017)
Article
Engineering, Electrical & Electronic
Ram Kumar, F. A. Talukdar, Abhishek Rajan, Anandini Devi, R. Raja
MICROSYSTEM TECHNOLOGIES-MICRO-AND NANOSYSTEMS-INFORMATION STORAGE AND PROCESSING SYSTEMS
(2020)
Article
Telecommunications
Ashish Pandey, Abhishek Rajan, Arnab Nandi, Valentina E. Balas
Summary: This article introduces the concept of supernodes and uses the Moth Flame Optimization algorithm to enhance the lifetime of heterogeneous WSNs. The performance of the Moth Flame Optimization algorithm is compared with other existing protocols, and the effects of varying populations of supernodes and sensor nodes on network metrics are analyzed. The influence of the number of hops on lifetime is also investigated considering two different base-station positions in WSNs.
WIRELESS PERSONAL COMMUNICATIONS
(2021)
Article
Computer Science, Information Systems
Ashish Pandey, Abhishek Rajan, Arnab Nandi, Valentina E. Balas
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
(2019)
Proceedings Paper
Computer Science, Theory & Methods
Ashish Pandey, Abhishek Rajan, Arnab Nandi
2018 7TH INTERNATIONAL CONFERENCE ON RELIABILITY, INFOCOM TECHNOLOGIES AND OPTIMIZATION (TRENDS AND FUTURE DIRECTIONS) (ICRITO) (ICRITO)
(2018)
Proceedings Paper
Energy & Fuels
Ashish Pandey, Abhishek Rajan, Arnab Nandi
2018 INTERNATIONAL CONFERENCE ON COMPUTING, POWER AND COMMUNICATION TECHNOLOGIES (GUCON)
(2018)
Article
Computer Science, Artificial Intelligence
Abhishek Rajan, K. Jeevan, T. Malakar
APPLIED SOFT COMPUTING
(2017)
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)
