Article
Green & Sustainable Science & Technology
Gourav Kumar Suman, Josep M. Guerrero, Om Prakash Roy
Summary: This study focuses on improving the stability of microgrids by developing frequency controller systems, comparing performance with various algorithms, and analyzing sensitivity under different scenarios of load and renewable uncertainties, ultimately determining more effective controllers.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Computer Science, Interdisciplinary Applications
Ruochen Sun, Qingyun Duan, Xiyezi Mao
Summary: Many multi-objective optimization problems in integrated environmental modeling and management involve complex constraints and different types of decision variables. This study presents an algorithm called MO-ASMOCH that can effectively solve these hybrid problems by using fewer model evaluations and achieving high-quality solutions.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Engineering, Marine
Pawel L. Manikowski, David J. Walker, Matthew J. Craven
Summary: Wind farm layout optimisation is a challenging and widespread problem that aims to achieve maximum power output and minimum cost. Single and multi-objective optimisation techniques, including NSGA-II, SPEA2, and PESA-II, have been used to achieve better results in different wind scenarios, with NSGA-II performing the best among the multi-objective algorithms. Extensive comparisons of past publications have also been made to evaluate the effectiveness of different algorithms.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2021)
Article
Engineering, Industrial
M. Rivier, P. M. Congedo
Summary: This paper presents the SABBa method to address constrained multi-objective optimization problems under uncertainty, aiming to improve accuracy and reduce computational costs using bounding boxes and surrogate-assisting strategy. The method demonstrates good performance in several analytical test cases and is successfully applied to three engineering applications.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Thermodynamics
Hongwei Li, Boshi Xu, Guolong Lu, Changhe Du, Na Huang
Summary: This paper presents a fast and systematic optimization approach for PEMFC by combining variance analysis, surrogate models, and NSGA-II. By optimizing power density, system efficiency, and oxygen distribution uniformity simultaneously, the study demonstrates the success of this method in solving time-consuming multi-optimization problems efficiently.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Construction & Building Technology
Zhaoyong Wang, Joshua Adriel Mulyanto, Chaorong Zheng, Yue Wu
Summary: In order to solve the multi-objective optimization problems of supertall buildings, an efficient multi-objective optimization method was developed using a surrogate model based on genetic algorithm optimized generalized regression neural network. The proposed framework, based on non-dominated sorting genetic algorithm and GA-GRNN surrogate model updating, was verified using experimental wind pressure data and analyzed for factors influencing optimization efficiency. The framework showed satisfactory optimization accuracy and efficiency, with the optimal proportion of initial sample points determined by the acquisition time of a single sample value and the total number of sample points.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Xianguo Wu, Lei Wang, Bin Chen, Zongbao Feng, YaWei Qin, Qiong Liu, Yang Liu
Summary: This study proposes a hybrid intelligence framework that combines random forest and non-dominant classification genetic algorithm II to optimize the shield construction parameters in metro construction. The results show that the framework effectively reduces surface settlement and improves safe driving speed. The framework can also serve as a support tool for real-time optimization and control of the shield construction parameters.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Construction & Building Technology
Pengcheng Wang, Zhongbing Liu, Dapeng Chen, Weijiao Li, Ling Zhang
Summary: This study introduces a simple and practical integral thermoelectric wall (ITEW) that eliminates heat losses or gains of the wall and provides indoor cooling and heating capacity. By optimizing key parameters, the cooling and heating performance of ITEW can be simultaneously improved.
ENERGY AND BUILDINGS
(2021)
Article
Thermodynamics
Shima Soleimani, Steven Eckels
Summary: This paper proposes a methodology for optimizing the three-dimensional geometry of micro-fin surfaces and provides a numerical model to determine the flow physics of optimal geometries. By applying a multi-objective optimization algorithm, the study identifies the best trade-off solutions between enhancement of Nusselt number and Fanning friction factor.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
(2022)
Article
Computer Science, Information Systems
Matheus F. Torquato, German Martinez-Ayuso, Ashraf A. Fahmy, Johann Sienz
Summary: This paper introduces a new approach for optimization and modeling of the EAF-based steel-making process using evolutionary computing and machine learning, achieving reduction in scrap cost and energy consumption per ton of scrap. The study utilized historical data from a steel plant and developed machine learning models for EAF and ladle furnaces, resulting in significant improvements in energy usage prediction and cost reductions for different steel grades.
Article
Engineering, Electrical & Electronic
Rui Dai, Yue Zhang, Tianyu Wang, Fengge Zhang, Chris Gerada, Yuan Zhang
Summary: HSPMM is gaining more attention for its high performance characteristics; the complex design of its rotor demands a new optimization method; the study shows the feasibility of using the MPSM method for optimizing HSPMM.
IET ELECTRIC POWER APPLICATIONS
(2021)
Article
Mechanics
Zheng Zhang, Chongjie Liao, Hao Chai, Xiangqi Ni, Kai Pei, Min Sun, Huaping Wu, Shaofei Jiang
Summary: The study proposes a multi-objective optimization technique for bistable laminates, utilizing surrogate models and the NSGA-II algorithm to obtain Pareto-optimal solutions. Experimental investigations validate the optimal designs of bistable laminates obtained through this technique.
COMPOSITE STRUCTURES
(2021)
Article
Computer Science, Interdisciplinary Applications
Maliki Moustapha, Alina Galimshina, Guillaume Habert, Bruno Sudret
Summary: Accounting for uncertainties is crucial for the safety of engineering structures. This study proposes a method for robust design optimization by considering quantiles of objective functions. By introducing the concept of common random numbers and using a surrogate-assisted approach, the computational cost of the optimization problem is reduced.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Mathematics
Mohamed El-Nemr, Mohamed Afifi, Hegazy Rezk, Mohamed Ibrahim
Summary: This paper presents an optimal design methodology for Switched Reluctance Motors (SRM) using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. The proposed design procedure considers various dimensions of SRM and includes three objective functions for maximum average torque, maximum efficiency, and minimum iron weight. Results show that the integration of NSGA-II and Finite Element Analysis (FEA) provides an effective approach to obtain optimal SRM design with improved torque and efficiency.
Article
Construction & Building Technology
Shixue Liang, Yiqing Cai, Zhengyu Fei, Yuanxie Shen
Summary: This study addresses the challenges of multi-objective optimization punching shear design of fiber-reinforced polymer (FRP) reinforced flat slabs by using a data-driven surrogate model. It employs Natural Gradient Boosting (NGBoost) model to predict the punching shear resistance of FRP reinforced flat slabs and demonstrates higher accuracy compared to other models. The study also reveals that the slab's effective depth is the primary factor affecting the punching shear resistance. Through the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm, the Pareto-optimal set of punching shear resistance and cost of FRP reinforced flat slabs is successfully obtained, with an increased effective depth shown to achieve higher punching shear resistance.
Article
Transportation Science & Technology
Una Benlic, Alexander E. I. Brownlee, Edmund K. Burke
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2016)
Article
Transportation Science & Technology
Alexander E. Brownlee, Michal Weiszer, Jun Chen, Stefan Ravizza, John R. Woodward, Edmund K. Burke
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2018)
Article
Operations Research & Management Science
Alexander E. I. Brownlee, Jerry Swan, Richard Senington, Zoltan A. Kocsis
OPTIMIZATION LETTERS
(2020)
Article
Transportation Science & Technology
Xinwei Wang, Alexander E. Brownlee, Michal Weiszer, John R. Woodward, Mahdi Mahfouf, Jun Chen
Summary: This study proposes a new model and algorithm for optimizing taxi time in airport ground movement, and empirical simulations demonstrate that the new method can more efficiently allocate routes and reduce the number of aircraft stops during taxiing.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Computer Science, Artificial Intelligence
Alexander E. I. Brownlee, Michael G. Epitropakis, Jeroen Mulder, Marc Paelinck, Edmund K. Burke
Summary: This study presents a systematic approach to software parameter optimization, implementing different techniques sequentially with rigorous analysis of the search space, allowing results to be explainable to end users and developers, enhancing confidence in optimal solutions, especially when they are counter-intuitive.
JOURNAL OF HEURISTICS
(2022)
Article
Computer Science, Artificial Intelligence
Xinwei Wang, Alexander Edward Ian Brownlee, Michal Weiszer, John R. Woodward, Mahdi Mahfouf, Jun Chen
Summary: Airports and their related operations are causing major concerns in air traffic management system due to predictability, safety, and environmental issues. This article proposes a new interval type-2 fuzzy logic-based map matching algorithm to optimize airport ground movement. Experimental results show that the designed fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust with a map matching accuracy of over 96% without compromising the run time.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Martin Fyvie, John A. W. Mccall, Lee A. Christie, Alexander E. I. Brownlee, Manjinder Singh
Summary: This article presents an approach to extract explanation supporting features using trajectory mining. The results show that this approach can capture key learning steps and solution variable patterns that explain the fitness function.
Article
Computer Science, Artificial Intelligence
Alexander E. Brownlee, Jonathan A. Wright, Miaomiao He, Timothy Lee, Paul McMenemy
APPLIED SOFT COMPUTING
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Jason Adair, Lexander E. Brownlee, Gabriela Ochoa
APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2018
(2018)
Proceedings Paper
Computer Science, Information Systems
Jason Adair, Alexander Brownlee, Fabio Daolio, Gabriela Ochoa
MACHINE LEARNING, OPTIMIZATION, AND BIG DATA, MOD 2017
(2018)
Proceedings Paper
Computer Science, Software Engineering
Saemundur O. Haraldsson, John R. Woodward, Alexander I. E. Brownlee
2017 IEEE/ACM 10TH INTERNATIONAL WORKSHOP ON SEARCH-BASED SOFTWARE TESTING (SBST)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Jason Adair, Alexander Brownlee, Gabriela Ochoa
ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS
(2017)
Proceedings Paper
Computer Science, Theory & Methods
John R. Woodward, Colin G. Johnson, Alexander E. I. Brownlee
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION)
(2016)
Proceedings Paper
Computer Science, Theory & Methods
John R. Woodward, Alexander E. I. Brownlee, Colin G. Johnson
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION)
(2016)
Proceedings Paper
Computer Science, Theory & Methods
John R. Woodward, Colin G. Johnson, Alexander E. I. Brownlee
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION)
(2016)
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)