Article
Business, Finance
Zhi Wang, Xuan Zhang, ZheKai Zhang, Dachen Sheng
Summary: This paper presents a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. It introduces quadratic weighting and a novel bivariate intensity model to measure default risk, and utilizes a multi-objective genetic algorithm to improve optimization efficiency.
BORSA ISTANBUL REVIEW
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhenping Chen, Zhenyu Zhang, Jinsen Xie, Qian Guo, Tao Yu, Pengcheng Zhao, Zijing Liu, Chao Xie
Summary: This study developed two multi-objective optimization strategies using genetic algorithm to efficiently and automatically optimize radiation shielding design, successfully balancing the relationship between shielding quality against the weight and volume of the shield, improving the efficiency and flexibility of radiation shielding design.
COMPUTER PHYSICS COMMUNICATIONS
(2021)
Review
Green & Sustainable Science & Technology
Rudai Shan, Lars Junghans
Summary: This study presents a systematic review of optimization methods for building facade optimization (BFO), comparing the efficiency and effectiveness of different algorithms. Key findings highlight the robust feasibility and effectiveness of optimization algorithms, methods, and techniques in resolving a diverse range of BFO challenges.
Article
Environmental Sciences
Tianqu Liu, Jinping Sun, Guohua Wang, Yilong Lu
Summary: This paper addresses the problem of maximizing waveform diversity gain in a phase-coded MIMO radar waveform set design. A multi-objective quantum genetic algorithm is proposed to solve the design problem. Compared to conventional approaches, the proposed algorithm can simultaneously minimize both PASR and PCCR of the waveform set.
Article
Biotechnology & Applied Microbiology
Ying Sun, Peng Huang, Yongcheng Cao, Guozhang Jiang, Zhongping Yuan, Dongxu Bai, Xin Liu
Summary: This paper proposes a genetic algorithm-based optimization method for ladle refractory lining structure and establishes a mathematical model for multi-objective optimization. By optimizing the design of the refractory lining, the thermal insulation performance and lightweight performance of the ladle can be improved, which is important for extending the service life of the ladle.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Mechanics
Ricardo Fitas, Goncalo das Neves Carneiro, Carlos Conceicao Antonio
Summary: The inclusion of uncertainty in structural design optimization has led to more complex formulations, where uncertainty quantification significantly impacts solution methods and computing times. Designs that maintain steady levels of performance under uncertainty are referred to as robust, and the combination of both robustness and performance optimality is known as Robust Design Optimization (RDO). This study proposes a new approach to RDO for angle-ply composite laminate structures, utilizing a hybridization of Particle Swarm Optimization and Genetic Algorithms.
COMPOSITE STRUCTURES
(2023)
Article
Multidisciplinary Sciences
Tong Wu, Jing Li, Xuan Qin
Summary: In order to enhance braking performance, engineers improve braking systems by upgrading structures and optimizing parameters, with multi-objective optimal design of electro-mechanical brake (EMB) parameters being an effective method. Research results show that this optimal design can reduce braking pressure response time, shorten stopping distance, increase mean fully developed deceleration, and reduce lateral displacement of the body.
Article
Engineering, Electrical & Electronic
Eric Diehl, Moritz von Tresckow, Lou Scholtissek, Dimitrios Loukrezis, Nicolas Marsic, Wolfgang F. O. Mueller, Herbert De Gersem
Summary: This work proposes the optimization of a quadrupole magnet's geometry using a genetic algorithm. The algorithm, known as NSGA-III, is adapted to handle multi-objective optimization problems. The objectives of the optimization are to ensure high magnetic field quality and cost-efficiency in the magnet design. The results of a magnetostatic finite element model are used to compute the field quality and incorporated into the optimization algorithm. Extensive analysis is conducted on the optimization results, including studying Pareto front movements and identifying the best designs.
ELECTRICAL ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Keyu Zhong, Guo Zhou, Wu Deng, Yongquan Zhou, Qifang Luo
Summary: The paper presents a multi-objective version of the marine predator algorithm, called MOMPA, which introduces an external archive and a top predator selection mechanism for optimization. The performance of the algorithm is evaluated on benchmark functions and engineering design problems, showing competitive results and outperforming other algorithms.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Computer Science, Information Systems
Xuewen Xia, Huixian Qiu, Xing Xu, Yinglong Zhang
Summary: In this paper, a multi-objective genetic algorithm (MOGA) is proposed and applied to optimize workflow scheduling problems under the cloud computing environment. An initialization scheduling sequence scheme is introduced to enhance search efficiency, and the longest common subsequence (LCS) is integrated into the genetic algorithm (GA) to achieve a balance between exploration and exploitation. Experimental results demonstrate that the proposed GALCS algorithm outperforms ordinary GA and other state-of-the-art algorithms in finding a better Pareto front.
INFORMATION SCIENCES
(2022)
Article
Thermodynamics
Mohamed Nejlaoui, Abdullah Alghafis, Hussain Sadig
Summary: The study focuses on developing FPCs with optimal performance, considering uncertainties and using a combined algorithm for multi-objective optimization. Results show that robust optimization design performs comparably to deterministic design with lower sensitivity to uncertainties.
Article
Thermodynamics
Heng-Yi Li, Yu-Ren Chen, Ming-Jui Tsai, Tsair-Fuh Huang, Chun-Liang Chen, Sheng-Fu Yang
Summary: This research proposes an optimal design framework combining analytical model, multi-objective optimization, and decision-making for optimizing the dehumidification performance and energy consumption of a desiccant wheel. An overall heat and mass balance-based model is used to derive the objective functions and constraints for the desiccant wheel. The non-dominated sorting genetic algorithm II (NSGA-II) is employed to calculate the Pareto optimal front of the two-objective optimization, and the results are analyzed using a psychrometric chart. The final optimal solution is obtained using the technique for order preference by similarity to ideal solution (TOPSIS) based on four criteria, resulting in further improvements on the outlet process air humidity ratio and the dehumidification coefficient of performance when applied to an existing example.
APPLIED THERMAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xiaoxia Huang, Kwon Ryong Hong, Jang Su Kim, Il Jong Choe
Summary: This paper discusses a multi-objective mean-variance model and its solution algorithms for project selection considering synergy under uncertain environment. The effect of uncertainty and synergy on project selection is analyzed and new solution algorithms are proposed. Numerical experiments show the performance of the proposed algorithms and a numerical example is given to demonstrate the validity of the model.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Fei Ming, Wenyin Gong, Yueping Yang, Zuowen Liao
Summary: This work proposes a test problem construction approach for constrained multimodal multi-objective optimization and creates a test suite containing 14 instances. A new evolutionary framework tailored for this kind of problem is also developed. Experimental results show that the proposed test suite is challenging and can motivate researchers to develop new algorithms. Furthermore, the superiority of our proposed framework demonstrates its effectiveness in handling constrained MMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)