Article
Computer Science, Artificial Intelligence
Hui Wang, Shuai Wang, Zichen Wei, Tao Zeng, Tingyu Ye
Summary: This paper proposes an improved many-objective artificial bee colony algorithm based on decomposition and dimension learning to solve many-objective optimization problems. The multi-objective problem is converted into several sub-problems by decomposition, and a new fitness function is defined. Elite solutions are selected based on their fitness values. The algorithm uses an elite set guided search strategy and dimension learning to improve convergence, and dynamically allocates computing resources in the scout bee stage. Experimental results show that this method outperforms seven other many-objective evolutionary algorithms.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Kai Li, Hui Wang, Wenjun Wang, Feng Wang, Zhihua Cui
Summary: This paper proposes an artificial bee colony algorithm based on a modified nearest neighbor sequence to enhance optimization capability. Experimental results show that the algorithm performs competitively on various benchmark problems and complex problems.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Chunfeng Wang, Pengpeng Shang, Peiping Shen
Summary: This paper presents a novel ABC algorithm based on Bayesian estimation (BEABC) to improve the performance of the original ABC algorithm. By replacing the selection probability with a probability calculated by Bayesian estimation and designing a directional guidance mechanism, BEABC achieves better results in single-objective, multi-objective, and real-world optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Jiaxu Ning, Haitong Zhao, Chang Liu
Summary: An improved exhausted food source identification mechanism based on space partitioning is designed to address the issue of inefficient exploration and excessive searching resources allocation in existing ABC algorithms. The mechanism is applied to both the basic ABC algorithm and a recently improved version, showing better performance in almost all functions on the CEC2015 test suit compared to the original ABC algorithms.
Article
Computer Science, Artificial Intelligence
Tingyu Ye, Wenjun Wang, Hui Wang, Zhihua Cui, Yun Wang, Jia Zhao, Min Hu
Summary: This article introduces a new artificial bee colony algorithm (RNSABC) based on random neighborhood structure to enhance the performance of the original ABC algorithm. The authors construct a random neighborhood structure and design an improved search strategy for optimization. Additionally, a depth-first search method is used to enhance the role of the onlooker bee phase. Experimental results demonstrate that RNSABC achieves competitive performance compared to nine other recent ABC variants.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Tao Zeng, Wenjun Wang, Hui Wang, Zhihua Cui, Feng Wang, Yun Wang, Jia Zhao
Summary: The paper introduces an efficient ABC algorithm named ASRGABC based on adaptive search strategy and random grouping mechanism. It adapts search strategy, introduces random grouping mechanism, and utilizes opposition-based learning to enhance the scout bee phase, outperforming thirteen other ABC variants in benchmark problem tests.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Xing Li, Shaoping Zhang, Le Yang, Peng Shao
Summary: An enhanced algorithm called EMABC-NS is proposed to improve the shortcomings of the artificial bee colony (ABC) algorithm in terms of convergence speed and exploitation ability for complex practical problems. By employing information from the global optimal individual and individuals in the neighborhood, as well as introducing a modification rate, EMABC-NS achieves better performance than other competitors and ranks first in the Friedman test.
Article
Computer Science, Artificial Intelligence
Xu Chen, Hugo Tianfield, Wenli Du
Summary: This paper introduces a novel bee-foraging learning PSO (BFL-PSO) algorithm with three different search phases, showing very competitive performance in terms of solution accuracy.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Yuan Zhao, Hong Liu, Kaizhou Gao
Summary: Simulation modeling is a crucial tool for studying crowd behavior and exploring emergency evacuation management methods. This paper proposes a new evacuation simulation method combining an improved artificial bee colony algorithm for dynamic path planning and SFM for simulating pedestrian movement, providing pedestrians with timely route selection. The new method shows superior performance in evacuating dense crowds efficiently in multiple scenarios and effectively shortening evacuation time.
APPLIED INTELLIGENCE
(2021)
Article
Mathematics
Ivona Brajevic
Summary: The improved ABC algorithm introduced modified search operator to enhance exploitation tendency, along with shuffle mutation operator for better balance between global exploration and local exploitation, resulting in superior performance compared to ABC and competitive results with other state-of-the-art algorithms in solving integer programming and minimax problems.
Article
Mathematics, Applied
Kalaipriyan Thirugnanasambandam, Rajakumar Ramalingam, Divya Mohan, Mamoon Rashid, Kapil Juneja, Sultan S. Alshamrani
Summary: This research aims to enhance the search capability of the swarm-based Artificial Bee Colony (ABC) algorithm in multimodal search space by applying two different strategies. The first strategy introduces cooperativeness in the scout bee phase and balances intensification and diversification through a self-adaptability approach. The second strategy controls the trap of local optima and promotes diversification without the pulse of intensification.
Article
Computer Science, Software Engineering
Jing Wang, Haoxiang Jie, Yue Jiang
Summary: Artificial bee colony algorithm is an intelligent optimization method that simulates the social behavior of bees. This article proposes a novel variant called EGABC that balances the exploitation and exploration abilities through flexible group guidance strategy. Experimental results show that EGABC significantly improves the performance of ABC algorithm.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Computer Science, Information Systems
Jose A. Concha-Carrasco, Miguel A. Vega-Rodriguez, Carlos J. Perez
Summary: Movie recommender systems often focus on a single objective, but this study presents a multi-objective recommender system that considers both liking probability and profit simultaneously. The proposed system outperforms existing algorithms in terms of accuracy and global profit, based on evaluations using MovieLens datasets.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Kavita Singh, Shyam Sundar
Summary: This paper introduces an artificial bee colony algorithm for the bounded diameter minimum spanning tree problem, utilizing permutation encoding and two neighborhood strategies to assist the algorithm in finding high-quality solutions more efficiently. Computational results show the effectiveness of this approach compared to existing methods.
Article
Computer Science, Information Systems
Weicun Zhang, Yanan Li
Summary: A many-objective artificial bee colony algorithm based on adaptive grid (MOABCAG) is proposed to enhance solution convergence and diversity by improving the location sharing mechanism and setting an adaptive grid search method. Comparing with other algorithms, MOABCAG shows better performance in solving many-objective optimization problems.
Article
Transportation
Kam K. H. Ng, C. K. M. Lee, S. Z. Zhang, K. L. Keung
Summary: This paper explores the efficient utilization of runway capacity in the context of rapid growth in the airline industry. By using dynamic runway configuration and semi-mixed runway design, significant reductions in flight tardiness were achieved in the test case.
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH
(2022)
Article
Statistics & Probability
Jia Xu, Liwei Liu, Kan Wu
Summary: This paper studies an M/G/1 retrial queueing system with modified multiple vacations, and provides the sufficient and necessary condition of system stability by constructing an embedded Markov chain. The distributions of the orbit size and the system size in steady-state are derived through the supplementary variable method. Some system performance measures and the Laplace-Stieltjes transform of sojourn time distribution are obtained.
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
(2023)
Article
Computer Science, Interdisciplinary Applications
Meimei Zheng, Xiaoqian Shi, Ershun Pan, Kan Wu
Summary: This study analyzes the production and order decisions in a manufacturer-retailer system, focusing on standard and customized products. The findings suggest that the manufacturer's willingness to produce either type of product depends on specific conditions. Additionally, a cost-sharing contract is proposed to coordinate decision-making in the supply chain and improve profits for both the manufacturer and retailer.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Management
Kan Wu, Edward Huang, Mengchang Wang, Meimei Zheng
Summary: Furnace scheduling is a critical aspect of semiconductor manufacturing, but it is a challenging problem due to complex constraints and a large solution space. This paper presents an efficient algorithm based on identified properties, which improves throughput rate and scheduling efficiency. The method has been implemented and validated in practical production lines.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Chemistry, Analytical
Chung-Han Chou, Tse-Hao Huang, Po-Chuan Hsieh, Natalie Yi-Ju Ho, Chung-An Chen, Kan Wu, Tsung-Ting Tsai
Summary: Cerebrospinal fluid (CSF) leakage is a common complication of spine surgery, and its diagnosis and treatment are challenging. A high-sensitivity lateral flow immunoassay (sLFIA) method for quantitatively detecting a specific CSF marker (BTP) was developed. The sLFIA method showed good sensitivity and specificity for diagnosing CSF leakage and assessing the severity of the leakage.
ANALYTICA CHIMICA ACTA
(2022)
Article
Engineering, Electrical & Electronic
Zhonghao Zhao, Carman K. M. Lee
Summary: This article proposes a new dynamic pricing framework for EV charging stations that offers multiple charging options to customers and aims to maximize the quality of service. It employs a customized deep reinforcement learning approach to solve the problem and demonstrates its effectiveness through simulation results.
IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION
(2022)
Article
Thermodynamics
Zhonghao Zhao, Carman K. M. Lee, Jiage Huo
Summary: This study addresses the optimal deployment of electric vehicle charging stations in the transportation and power distribution networks, which is a critical issue for the mass adoption of EVs. A finite-discrete Markov decision process formulation is proposed in a reinforcement learning framework to solve the curse of dimensionality problem. The proposed approach, which utilizes a LSTM-based recurrent neural network with an attention mechanism, outperforms other baseline approaches in terms of solution quality and computational time.
Article
Engineering, Industrial
Wenqin Zhao, Yaqiong Lv, Jialun Liu, Carman K. M. Lee, Lei Tu
Summary: Effective fault diagnosis plays a crucial role in maximizing economic benefits by ensuring the stability of machinery systems. Early detection of faults in key components, such as rolling bearings, helps prevent accidents and optimize maintenance efficiency.
QUALITY ENGINEERING
(2023)
Article
Mathematics
Adegoke A. Muideen, Carman Ka Man Lee, Jeffery Chan, Brandon Pang, Hafiz Alaka
Summary: This paper introduces a new machine learning model for predicting air pressure system (APS) failure in the automotive industry. The proposed model combines a broad learning system and logistic regression classifier, and uses principal component analysis to reduce data dimension. Experimental results validate the performance of the model.
Article
Engineering, Civil
Zhonghao Zhao, Carman K. M. Lee, Jingzheng Ren, Yung Po Tsang
Summary: This study aims to determine the best deployment plan for EV fast charging stations in a transportation network with limited budget. The objective is to maximize the quality of service with respect to waiting time and range anxiety from the perspective of EV customers. The study proposes a novel reinforcement learning framework using a finite discrete Markov decision process to address the curse of dimensionality problem and a recurrent neural network with an attention mechanism for unsupervised learning.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Lucas Kwai Hong Lui, C. K. M. Lee
Summary: This research investigated a mathematical model of earphone design with principal component analysis and formulated a predictive model for sound quality indicators. The study simplified the design problem and utilized principal component analysis to decrease the number of input variables. The results showed suboptimal predictive accuracy for the sound quality indicators but obtained a simplified formulation.
APPLIED SCIENCES-BASEL
(2023)
Article
Chemistry, Multidisciplinary
Eric Wai, C. K. M. Lee
Summary: This study introduces a multilayered cybersecurity framework to strengthen SCADA environments by implementing granular access controls, network micro-segmentation, anomaly detection, encrypted communications, and legacy system upgrades. The results show improved security with 57.4% fewer unauthorized access events, 41.2% faster threat containment, and 79.2% fewer hacking attempts, highlighting the effectiveness of this approach.
APPLIED SCIENCES-BASEL
(2023)
Article
Mathematics
Muhammad Waseem, Jingyuan Huang, Chak-Nam Wong, C. K. M. Lee
Summary: Due to the complexity of the aging process, maintaining the health of lithium-ion batteries is a significant challenge. This study proposes a new method for estimating the health status of batteries using hybrid Grey Wolf Optimization with Bayesian Regularized Neural Networks. The method extracts health features from the battery charging-discharging process and selects the most relevant features to explain battery aging. The proposed technique shows higher accuracy than existing approaches based on simulation results.
Article
Social Sciences, Interdisciplinary
Sheron K. H. Sit, Carman K. M. Lee
Summary: The growing consumer demand for unique products has made customization and personalization essential in manufacturing. Industry 5.0 emphasizes the importance of human workers and social sustainability in adapting to these changes. This study introduces a digital twin design tailored for low-volume, high-mix production, focusing on the collaboration between human expertise and advanced technologies.
Article
Management
T. T. Yang, Y. P. Tsang, C. H. Wu, K. T. Chung, C. K. M. Lee, S. S. M. Yuen
Summary: This research develops a mixed reality-based online pallet loading system supported by deep reinforcement learning technology and online algorithms. It can dynamically decide cargo placements and orientations without prior information, increasing space utilisation and achieving optimal palletisation.
OPERATIONS MANAGEMENT RESEARCH
(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)
