Article
Engineering, Multidisciplinary
Abdullah Shaheen, Ragab El-Seheimy, Salah Kamel, Ali Selim
Summary: This article presents a modified Jellyfish Search (MJFS) algorithm for optimal network reconfiguration (ONR) in Medium Voltage Distribution Feeders (MVDFs) to improve reliability and reduce power losses. The algorithm introduces quasi-oppositional-based learning and social neighborhood strategies, and uses a proposed bus-line feeding matrix (BLFM) for simulation evaluation. The experimental results show significant improvements over traditional JFS and other optimization algorithms in reducing power losses and improving system stability.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Rahim Fathi, Behrouz Tousi, Sadjad Galvani
Summary: This paper presents an optimal and simultaneous allocation of photovoltaic panel (PV) and wind turbine (WT) with the reconfiguration of radial distribution networks. The improved salp swarm algorithm (ISSA) is used for optimization and it is implemented on IEEE 33 and 69 bus distribution networks. The results show that ISSA can find the optimal location and size of renewable units and achieve the best network configuration.
APPLIED SOFT COMPUTING
(2023)
Article
Multidisciplinary Sciences
Kaifeng Geng, Li Liu, Zhanyong Wu
Summary: This study considers the distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent setup times, considering factory eligibility constraints under time of use price. It proposes a multi-objective Artificial Bee Colony Algorithm to optimize both the makespan and total energy consumption. The algorithm demonstrates its effectiveness in solving the scheduling problem through extensive experiments.
SCIENTIFIC REPORTS
(2022)
Article
Energy & Fuels
Gilberto Filho, Henrique Pires Correa, Flavio Henrique Teles Vieira
Summary: This paper proposes a heuristic-based approach to control reactive power injection in distributed photovoltaic generation, aiming to reduce electrical losses and minimize voltage deviation. Experimental results demonstrate that the proposed method consistently improves grid performance, reducing active power and enhancing voltage stability.
Article
Engineering, Electrical & Electronic
Masoud Dashtdar, Mohit Bajaj, Seyed Mohammad Sadegh Hosseinimoghadam, Irfan Sami, Subhashree Choudhury, Ateeq Ur Rehman, B. Srikanth Goud
Summary: This article discusses the use of UPQC to address distribution network issues through genetic algorithm-based network reconfiguration and UPQC optimal placement. The study is conducted in three steps, with the final results showing the method outperforming other approaches in terms of efficiency and effectiveness.
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS
(2021)
Article
Engineering, Multidisciplinary
Juan Wen, Xing Qu, Lin Jiang, Siyu Lin
Summary: This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The mechanism includes dynamic topology analysis, network reconfiguration, and network optimization to achieve the maximization of loads restored and minimization of switch operations.
MATHEMATICAL PROBLEMS IN ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Abdullah M. Shaheen, Abdallah M. Elsayed, Ragab A. El-Sehiemy, Salah Kamel, Sherif S. M. Ghoneim
Summary: A modified marine predators optimizer (MMPO) is proposed for simultaneous distribution network reconfiguration (DNR) associated with the allocation of distributed generators (DGs). The results show a great improvement over the standard marine predators optimizer (MPO) and demonstrate the superiority of the proposed MMPO for simultaneous DNR and DG allocation.
ENGINEERING OPTIMIZATION
(2022)
Article
Engineering, Electrical & Electronic
Mario A. Mejia, Leonardo H. Macedo, Gregorio Munoz-Delgado, Javier Contreras, Antonio Padilha-Feltrin
Summary: This study presents a novel mixed-integer linear programming model for medium-term reinforcement planning of active distribution networks, taking into account multiple investment options and CO2 emission limits. Uncertainties are addressed through scenario-based stochastic optimization. Modeling the load as voltage-dependent and integrating network reconfiguration into planning actions can help achieve a more environmentally friendly and cost-effective network.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Energy & Fuels
Tomasz Szczegielniak, Dariusz Kusiak, Pawel Jablonski
Summary: This paper proposed an analytical method to predict power losses and temperature distribution in medium voltage cables, which was validated through testing and laboratory measurements with an accuracy within 10%. The method is suitable for accurately predicting cable temperatures.
Article
Engineering, Electrical & Electronic
Panit Prukpanit, Phisan Kaewprapha, Nopbhorn Leeprechanon
Summary: The GMS model proposed in this paper, based on a global criterion approach, provides a better solution for GenCos by balancing the maximization of the GenCo's annual return and the probability of no unexpected DG failures. System reserve is considered a reliability constraint, with surplus reserve exchangeable with the main grid to support alternative energy sources and ensure continuous operation of distributed generators.
IET GENERATION TRANSMISSION & DISTRIBUTION
(2021)
Article
Engineering, Electrical & Electronic
Haiguo Li, Pengfei Yao, Zihan Gao, Fei Wang
Summary: This article introduces the insulation design of a grid-side filter inductor for a 13.8-kV power conditioning system converter, considering the insulation requirements imposed by the grid and various factors. The designed inductors have been validated through multiple tests.
IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Shubham Gupta, Vinod Kumar Yadav, Madhusudan Singh
Summary: This article proposes a novel method based on the Shannon entropy formula to determine the relative influence level of indices in multiobjective optimally distributed generation unit(s) placement problems.
IEEE SYSTEMS JOURNAL
(2023)
Article
Engineering, Civil
Dongying Yang, Sirong Yi, Qing He, Dewei Liu, Yifeng Wang
Summary: This paper proposes a railway alignment optimization model based on multiobject bi-level programming (MBRAO) to consider both ecological and economic factors in the horizontal and vertical alignment optimization, and applies it in multiple stages to find the optimal number of intersection points.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Correction
Green & Sustainable Science & Technology
Yonglei Zhang, Xibo Yuan, Mo Al-Akayshee
Summary: This article addresses errors in [1], specifically regarding the mistyped affiliation information of the first author in the footnote. The corrected sentence should read...
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Correction
Green & Sustainable Science & Technology
Yonglei Zhang, Xibo Yuan, Mo Al-Akayshee
Summary: This article corrects errors found in [1], specifically a typographical error in the first author's affiliation information in the footnote.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Geography
Jamshid Maleki, Zohreh Masoumi, Farshad Hakimpour, Carlos A. Coello Coello
Summary: This study aims to improve the efficiency of urban land use planning through enhancing the NSGA-III algorithm as a many-objective optimization approach. By considering five objective functions and testing the algorithm on real datasets, the results show enhanced convergence and diversity compared to traditional algorithms like NSGA-II. The optimized solutions obtained can aid decision-makers in achieving sustainable development in urban construction.
TRANSACTIONS IN GIS
(2022)
Article
Computer Science, Artificial Intelligence
Ali Ahrari, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This study introduces a second variant of the successful RS-CMSA-ES method, called RS-CMSA-ESII, which improves upon certain components and enhances the performance of the method in multimodal optimization.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Songbai Liu, Qiuzhen Lin, Kay Chen Tan, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes a fuzzy decomposition-based MOEA that estimates the population's shape using fuzzy prediction and selects weight vectors to fit the Pareto front shapes of different multi-objective optimization problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Automation & Control Systems
Lijia Ma, Xiao Zhang, Jianqiang Li, Qiuzhen Lin, Maoguo Gong, Carlos A. Coello Coello, Asoke K. Nandi
Summary: This article studies the robustness and resilience of multiplex networks in the presence of node cascading failures caused by coupling node relationships and community structures. A node protection strategy and a degree-based simulated annealing algorithm are proposed to improve network robustness and resilience. Experimental results show the vulnerability of networks to unpredictable damage under these circumstances, as well as the superiority of the proposed algorithm over existing ones.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Qiuzhen Lin, Xunfeng Wu, Lijia Ma, Jianqiang Li, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an ensemble surrogate-based framework for solving computationally expensive multiobjective optimization problems (EMOPs). The framework trains a global surrogate model and multiple surrogate submodels to enhance prediction accuracy and reliability. Experimental results demonstrate the advantages of this approach in solving EMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Automation & Control Systems
Forhad Zaman, Saber Elsayed, Ruhul Sarker, Daryl Essam, Carlos A. Coello Coello
Summary: This article introduces a novel auto-configured multioperator evolutionary approach for handling disruptions in project scheduling, which outperforms state-of-the-art algorithms in terms of solution quality.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Information Systems
Eduardo Fernandez, Jorge Navarro, Efrain Solares, Carlos A. Coello Coello, Raymundo Diaz, Abril Flores
Summary: Multicriteria sorting methods based on interval-based outranking approach are proposed, and parameter values are elicited using preference disaggregation paradigm and evolutionary algorithms. The proposed method effectively restores assignment examples and improves the ability to assign unknown actions.
Article
Automation & Control Systems
Lingjie Li, Qiuzhen Lin, Zhong Ming, Ka-Chun Wong, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an immune-inspired resource allocation strategy to better balance convergence and diversity in many-objective optimization. By defining the diversity distances of solutions, resource allocation is realized using an immune cloning operator to explore sparse regions of the search space. A novel archive update mechanism is also designed to provide high-quality solutions. The experimental results validate the superiority of this method in solving complex MOPs with 5 to 15 objectives.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Rihab Said, Maha Elarbi, Slim Bechikh, Carlos Artemio Coello Coello, Lamjed Ben Said
Summary: Discretization-based feature selection methods have a drawback of deleting important features during the encoding process. To address this issue, a bilevel optimization algorithm called Bi-DFS is proposed, which performs feature selection at the upper level and discretization at the lower level. Experimental results demonstrate that Bi-DFS outperforms existing methods in terms of classification accuracy, generalization ability, and feature selection bias.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Multidisciplinary Sciences
Joel Artemio Morales-Viscaya, Adan Antonio Alonso-Ramirez, Marco Antonio Castro-Liera, Juan Carlos Gomez-Cortes, David Lazaro-Mata, Jose Eleazar Peralta-Lopez, Carlos A. Coello A. Coello, Jose Enrique Botello-Alvarez, Alejandro Israel Barranco-Gutierrez
Summary: Fuzzy systems are widely used due to their robust, accurate, and easy-to-evaluate models that capture real-world uncertainty better than classical alternatives. This study proposes a new methodology for tuning fuzzy models using optimization techniques, resulting in better performance than existing strategies. By considering symmetry and equispacing, the membership functions are simplified, and a gradient descent method is used for optimization. The proposed strategy outperforms other methods, achieving at least 28% improvement in performance across all case studies in terms of RMSE.
Article
Automation & Control Systems
Lingjie Li, Yongfeng Li, Qiuzhen Lin, Songbai Liu, Junwei Zhou, Zhong Ming, Carlos A. Coello Coello
Summary: This research proposes a new neural net-enhanced competitive swarm optimizer (NN-CSO) to address the neglect of the evolution of winner particles in traditional CSOs. Experimental results show that NN-CSO significantly improves the performance of CSOs and outperforms other large-scale multiobjective evolutionary algorithms and model-based evolutionary algorithms.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Qiuzhen Lin, Zhongjian Wu, Lijia Ma, Maoguo Gong, Jianqiang Li, Carlos A. Coello Coello
Summary: This article proposes a new multiobjective multitasking evolutionary algorithm, MMTEA-DTS, which uses decomposition-based transfer selection. The algorithm decomposes all tasks into subproblems and quantifies the transfer potential of each solution based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected for knowledge transfer to improve search efficiency.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Antonio J. Nebro, Jesus Galeano-Brajones, Francisco Luna, Carlos A. Coello Coello
Summary: This study demonstrates the improved performance of the NSGA-II algorithm in large-scale optimization problems. By utilizing automated algorithmic tuning tools and a highly configurable version of NSGA-II, the experimental results show that the algorithm performs well in solving both test problems and real-world problems.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos Artemio Coello Coello
Summary: HDE is a multi-objective evolutionary algorithm that turns its selection process into a linear assignment problem. This study identifies two drawbacks in its selection process and proposes an algorithm using the hypervolume indicator to address these drawbacks.
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT II
(2022)
Article
Computer Science, Artificial Intelligence
Lijia Ma, Yuchun Ma, Qiuzhen Lin, Junkai Ji, Carlos A. Coello Coello, Maoguo Gong
Summary: In this paper, a novel generative adversarial nets learning framework (SNEGAN) is proposed for signed network embedding, preserving link structures signed by positive or negative labels. Extensive experiments demonstrate the superiority of SNEGAN over the state-of-the-art NE methods in link (sign) prediction and reconstruction tasks.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2022)
