Article
Energy & Fuels
Layon Mescolin de Oliveira, Ivo Chaves da Silva Junior, Ramon Abritta
Summary: This paper presents a new strategy to reduce the search space in the thermal unit commitment problem by obtaining a relevance matrix through a constructive heuristic based on sensitivity indexes. The proposed method reduces complexity and improves computational efficiency.
Article
Chemistry, Multidisciplinary
Whei-Min Lin, Chung-Yuen Yang, Ming-Tang Tsai, Yun-Hai Wang
Summary: This study proposes a method combining DPSO and SQP for solving unit commitment problems for ancillary services, with effective results demonstrated using real data. The research shows that costs with ancillary services are lower than those without ancillary services.
APPLIED SCIENCES-BASEL
(2021)
Article
Energy & Fuels
Aml Sayed, Mohamed Ebeed, Ziad M. Ali, Adel Bedair Abdel-Rahman, Mahrous Ahmed, Shady H. E. Abdel Aleem, Adel El-Shahat, Mahmoud Rihan
Summary: The paper proposes a hybrid optimization technique, MPSO-EO, to solve the unit commitment problem (UCP) under deterministic and stochastic load demand, which outperforms standard EO with significant cost savings. The simulation results demonstrate the fairly good performance of MPSO-EO in solving UCP compared to standard EO and other reported techniques.
Article
Multidisciplinary Sciences
Jun Long Peng, Xiao Liu, Chao Peng, Yu Shao
Summary: This article proposes and solves a multi-skill resource-based multi-modal project scheduling problem using a hybrid quantum algorithm. The experimental results demonstrate the effectiveness and superiority of the proposed algorithm.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Mohammad Noroozi, Hamed Mohammadi, Emad Efatinasab, Ali Lashgari, Mahdiyeh Eslami, Baseem Khan
Summary: The Golden Search Optimization Algorithm (GSO) is an effective population-based optimization algorithm that uses random solutions and a simple mathematical model to reach global optimum. The algorithm utilizes a transfer operator for adaptive step size adjustment to balance explorative and exploitative behavior in the search.
Article
Computer Science, Information Systems
Ting Wang, Peng Shao, Shanhui Liu, Guangquan Li, Fuhao Yang
Summary: This paper proposes a Multi-Mechanism Particle Swarm Optimization (HGSPSO) algorithm that optimizes the position update formula and dynamically updates inertia weights to accelerate convergence and help particles jump out of local extrema. Experimental results show that this algorithm outperforms five comparison algorithms in all evaluation metrics and assessment schemes.
Article
Chemistry, Multidisciplinary
Erica Ocampo, Chien-Hsun Liu, Cheng-Chien Kuo
Summary: The PSPSO introduces a two-fold searching mechanism to increase the search capability of Particle Swarm Optimization, avoiding premature convergence and simplifying communication among particles. Results show the effectiveness of PSPSO in comparison with published PSO variants.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Hassan Saadaoui, Faissal El Bouanani, Elmehdi Illi
Summary: This paper introduces an optimization strategy for searching moving targets using cooperative UAVs in unknown environments, leveraging a decentralized target search model and cooperative PSO algorithm to quickly and accurately locate the target. The simulation results demonstrate that this strategy outperforms other well-known target search methods in terms of performance and computational complexity.
Article
Computer Science, Artificial Intelligence
Yifeng Li, Ying Tan
Summary: In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed, and implemented. Experimental results show that the proposed algorithm outperforms previous variants of fireworks algorithm significantly, and achieves competitive results compared with state-of-the-art evolutionary algorithms.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Davoud Sedighizadeh, Ellips Masehian, Mostafa Sedighizadeh, Hossein Akbaripour
Summary: The Particle Swarm Optimization (PSO) algorithm, a nature-inspired meta-heuristic, has evolved into various variants due to its flexibility in parameters and concepts. The Generalized Particle Swarm Optimization (GEPSO) algorithm enriches the original PSO by incorporating new terms and dynamic inertia weight updates, leading to improved performance in continuous space optimization.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
Article
Green & Sustainable Science & Technology
Himanshu Anand, Nitin Narang, J. S. Dhillon
Summary: Environmental concerns have led researchers to consider pollutants emission as a key objective in cogeneration-based unit commitment problems. This has resulted in the extension of single objective CBUCP into a multi-objective optimization problem, utilizing a combination of binary and continuous particle swarm optimization methods along with global optimization techniques.
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2022)
Article
Computer Science, Artificial Intelligence
You Li, Huaxiong Li, Bo Wang, Min Zhou, Mei Jin
Summary: This study establishes a multi-objective unit commitment model taking into account low cost, high reliability, and low pollution targets, involving pricing support for ultra-low emissions thermal units and a Value-at-Risk-based measurement for system reliability. A multi-objective particle swarm optimization algorithm is used, with demonstrated effectiveness through two case studies.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Anuran Chakraborty, Kushal Kanti Ghosh, Rajonya De, Erik Cuevas, Ram Sarkar
Summary: Class imbalance is a prevalent issue in various domains, where traditional supervised machine learning algorithms often fall short. This paper introduces an undersampling approach based on Particle Swarm Optimization, showing promising performance on imbalanced datasets.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
An-Da Li, Bing Xue, Mengjie Zhang
Summary: This paper proposes an improved sticky binary PSO algorithm for feature selection problems, which aims to enhance evolutionary performance through new mechanisms such as an initialization strategy, dynamic bits masking, and genetic operations. Experimental results show that ISBPSO achieves higher accuracy with fewer features and reduces computation time compared to benchmark PSO-based FS methods.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Multidisciplinary
Abidhan Bardhan, Priyadip Manna, Vinay Kumar, Avijit Burman, Bojan Zlender, Pijush Samui
Summary: Reliability analysis of Piled Raft Foundations (PRFs) based on settlement criteria was conducted using a hybrid soft computing model integrating artificial neural network (ANN) and equilibrium optimizer (EO). The results showed that the proposed hybrid model ANN-EO outperformed conventional approaches, with a high accuracy in predicting settlement values. This study contributes significantly to the field of reliability analysis of piled raft systems, which is relatively scarce in existing literature.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2021)
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)