Article
Thermodynamics
Ryan M. Ogren, Song-Charng Kong
Summary: In this paper, modified artificial bee colony (ABC) algorithm and cooperative particle swarm optimization (CPSO) algorithm were used to optimize triple and quadruple injection routines for modern engines, achieving significant reductions in emissions in a shorter amount of time compared to traditional testing methods.
INTERNATIONAL JOURNAL OF ENGINE RESEARCH
(2021)
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.
Review
Computer Science, Interdisciplinary Applications
Janmenjoy Nayak, H. Swapnarekha, Bighnaraj Naik, Gaurav Dhiman, S. Vimal
Summary: This article presents an in-depth analysis of the Particle Swarm Optimization (PSO) algorithm and its developments in different application domains. PSO is highly popular due to its simple structure and few algorithmic parameters, and it has shown excellent performance in areas such as networking, robotics, and image segmentation. The paper discusses the evolution of PSO and its improved variants, providing a scope for further development and inspiring researchers and practitioners to find innovative solutions for complex problems in various domains using PSO.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
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
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
Mathematics, Applied
Guoming Du, Jiaxi Lu
Summary: This paper focuses on the spatial layout planning of urban public service facilities and compares the artificial bee colony algorithm with the particle swarm optimization algorithm. The experimental results show that the artificial bee colony algorithm performs better.
JOURNAL OF NONLINEAR AND CONVEX ANALYSIS
(2022)
Proceedings Paper
Automation & Control Systems
Imen Oueslati, Moez Hammami
Summary: Hyperheuristics based on honey bees behavior were proposed, and they showed good performance on MAX-SAT and Bin Packing problems compared to other hyperheuristics in the CHeSC competition.
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS (KSE 2021)
(2021)
Article
Environmental Sciences
Christopher Mayack, Anthony Macherone, Asal Ghaffari Zaki, Elif Filiztekin, Burcu Ozkazanc, Yasameen Koperly, Sassicaia J. Schick, Elizabeth J. Eppley, Moniher Deb, Nicholas Ambiel, Alexis M. Schafsnitz, Robert L. Broadrup
Summary: The study used biomarkers to predict pesticide exposure and diseases in bees, and identified chemical features and biological pathways through a systems biology approach. Novel external environmental exposures associated with bee diseases and pesticide exposures were found, revealing previously unknown connections to bee health. The exposure-outcome paradigm was highlighted for identifying interactions responsible for honey bee health decline.
Review
Automation & Control Systems
Jun Tang, Gang Liu, Qingtao Pan
Summary: Swarm intelligence algorithms are a subset of artificial intelligence that has gained popularity for solving optimization problems and has been widely utilized in various applications. This review summarizes the most representative swarm intelligence algorithms and their successful applications in engineering fields, providing insights into future trends and prospects for development.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Biochemical Research Methods
Karina Arias-Calluari, Theotime Colin, Tanya Latty, Mary Myerscough, Eduardo G. G. Altmann
Summary: A quantitative understanding of bee colony dynamics is crucial for improving bee health and pollination services. This study combines theoretical modeling and statistical analysis to interpret the state of bee colonies using intra-day weight variation data. The results show that crucial indicators of colony health can be estimated, providing early warning indicators of colony failure.
PLOS COMPUTATIONAL BIOLOGY
(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
Xinfang Ji, Yong Zhang, Dunwei Gong, Xiaoyan Sun
Summary: This article proposes a dual-surrogate-assisted cooperative particle swarm optimization algorithm for expensive multimodal optimization problems, combining dual-population cooperative particle swarm optimizer and modal-guided dual-layer cooperative surrogate model, with a hybrid strategy for detecting new modalities. Experimental results show that the algorithm can obtain multiple highly competitive optimal solutions at a low computational cost.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Geosciences, Multidisciplinary
K. Geetha, Malaya Kumar Hota, Dimitrios A. Karras
Summary: This article introduces a method using swarm optimization algorithms to estimate wavelet transform parameters for reducing noise in seismic signals. The effectiveness of this method is demonstrated through analysis of seismic traces and seismic sections.
JOURNAL OF APPLIED GEOPHYSICS
(2023)
Review
Automation & Control Systems
Ebubekir Kaya, Beyza Gorkemli, Bahriye Akay, Dervis Karaboga
Summary: The ABC algorithm is a popular optimization algorithm that has been successfully applied to solve real-world problems. This study examines combinatorial optimization approaches based on the ABC algorithm, provides summaries of related studies, and introduces the ABC algorithm-based approaches used. The study also evaluates mechanisms to improve the local search capability of the ABC algorithm and analyzes neighborhood operators, selection schemes, initial populations determination approaches, hybrid approaches, and test instances used in evaluating the performances of ABC algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Mathematics
Omer Ali, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri, Imran Ashraf
Summary: This study introduces a competitive coevolution process to enhance the capability of Phasor PSO (PPSO) for global optimization problems. Experimental results show that the improved competitive multi-swarm PPSO (ICPPSO) algorithm achieves a dominating performance, with average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO.
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)