Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Zaid Abdi Alkareem Alyasseri, Sharif Naser Makhadmeh, Mohammed El-Abd
Summary: This paper proposes an island neighboring heuristics harmony search algorithm (INHS) to solve blocking flow-shop scheduling problem. The algorithm enhances its performance by diversifying the population using the island model and improving solution quality using neighboring heuristics. Experimental results demonstrate the efficiency and competitiveness of the proposed algorithm in solving instances from different datasets.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Thaer Thaher, Alaa Sheta, Mohammed Awad, Mohammed Aldasht
Summary: This paper introduces an enhanced variant of the Crow Search Algorithm, called Enhanced CSA (ECSA), which incorporates the cooperative island model (iECSA) to improve its search capabilities and avoid premature convergence. Experimental results demonstrate that iECSA outperforms other algorithms on the majority of standard test functions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Electrical & Electronic
Mohammed Azmi Al-Betar
Summary: Economic load dispatch (ELD) is a critical problem in the power system domain, which is tackled using optimization algorithms such as the island based harmony search algorithm (iHS). This paper demonstrates the feasibility and efficiency of iHS by analyzing its performance with different parameters and comparing the results with other established methods.
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Jafar Gholami, Kareem Kamal A. Ghany, Hossam M. Zawbaa
Summary: Harmony search (HS) algorithm is a population-based optimization algorithm inspired by musical harmony, and a new algorithm called IMGHSA has been proposed in this paper, which outperforms existing HS variants in optimizing objective functions with better robustness and convergence performance.
Article
Chemistry, Multidisciplinary
Kathiresan Gopal, Lai Soon Lee, Hsin-Vonn Seow
Summary: Epidemiological models are crucial in understanding the spread and severity of infectious disease pandemics like COVID-19. Mathematical modeling of infectious diseases, typically in compartmental form, heavily relies on accurate estimation of epidemiological parameters. This study formulates parameter estimation as an optimization problem and applies the Harmony Search algorithm to obtain optimized parameters, showing it as a potential alternative tool for parameter estimation in compartmental epidemiological models.
APPLIED SCIENCES-BASEL
(2021)
Article
Green & Sustainable Science & Technology
Ayla Ocak, Sinan Melih Nigdeli, Gebrail Bekdas, Sanghun Kim, Zong Woo Geem
Summary: In this study, an adaptive harmony search algorithm was used to optimize a seismic isolator placed on the base of a structure under various earthquake records. The study found that isolators with low damping ratios require more ductility, and as the damping ratio increases, further restriction of the isolator's movement increases the control efficiency.
Review
Computer Science, Artificial Intelligence
Feng Qin, Azlan Mohd Zain, Kai-Qing Zhou
Summary: This article systematically reviews the harmony search (HS) algorithm and its variants from three aspects: describing the basic HS principle, discussing the impact of HS improvement on algorithm performance, and analyzing the characteristics and applications of HS variants. It is found that the improvement of HS mainly focuses on parameter enhancement and the integration with other metaheuristic algorithms, providing future directions for enhancing HS.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Chemistry, Multidisciplinary
Ayla Ocak, Sinan Melih Nigdeli, Gebrail Bekdas, Sanghun Kim, Zong Woo Geem
Summary: In this study, the tuned liquid damper (TLD) device was optimized using the harmony search algorithm, and further improved by the adaptive harmony search algorithm. The results showed that the adaptive harmony search algorithm resulted in smaller displacements in the seismic analysis compared to the classical harmony search.
APPLIED SCIENCES-BASEL
(2022)
Article
Biochemical Research Methods
Biraj Pandey, Marius Pachitariu, Bingni W. Brunton, Kameron Decker Harris
Summary: This study models the receptive fields of sensory neurons in a way that incorporates randomness and connects to the theory of artificial neural networks. The models enhance signal and remove noise, enabling more efficient learning in artificial tasks. This research has significance for both neuroscience and machine learning communities.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Environmental Studies
Sina Shaffiee Haghshenas, Sami Shaffiee Haghshenas, Zong Woo Geem, Tae-Hyung Kim, Reza Mikaeil, Luigi Pugliese, Antonello Troncone
Summary: This study utilized machine learning techniques to assess the stability condition of homogeneous slopes and found that the Harmony Search algorithm is an efficient approach for training K-means algorithms, providing insight into evaluating slope stability.
Article
Computer Science, Artificial Intelligence
Weixiong Huang, Juan Zou, Huanrong Tang, Jinhua Zheng, Fan Yu
Summary: This paper proposes an enhanced auxiliary population search algorithm (EAPS) to solve complex constrained multiobjective optimization problems. The algorithm improves the distribution of the main population by introducing an auxiliary population and provides favorable diversity information while balancing the convergence and feasibility of solutions.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Qidan Zhu, Xiangmeng Tang, Ahsan Elahi
Summary: The study proposed the K-DBSCAN clustering method, utilizing the novel harmony search (novel-HS) optimization algorithm to improve the clustering parameters of DBSCAN, achieving better clustering results with K classifications. Experimental results demonstrated that this designed clustering method outperforms others and can be considered as a new clustering scheme for further research.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Azad A. Ameen, Tarik A. Rashid, Shavan Askar
Summary: CDDO-HS is a hybrid model that combines CDDO and HS standards to optimize children's drawing development. The model addresses the issues of low performance and stagnation in CDDO by relocating the pattern size (PS) to the algorithm's core and adding HS to enhance and update the solution in the exploration phase.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Qidan Zhu, Xiangmeng Tang
Summary: The improved harmony search algorithm AHS-HCM overcomes the shortcomings of basic HS in terms of low optimization accuracy and risk of falling into local optimum by introducing a hybrid convergence mechanism, and experiments have shown its effectiveness compared to other HS variants and population-based algorithms.
Review
Mathematics
Mohammad Nasir, Ali Sadollah, Przemyslaw Grzegorzewski, Jin Hee Yoon, Zong Woo Geem
Summary: Researchers have been incorporating metaheuristic optimization algorithms and fuzzy logic theory in recent years, particularly the combination of harmony search algorithm and fuzzy logic theory. Models derived from this integration have shown promising performance in various studies, suggesting the potential for solving optimization problems effectively.
Article
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Xin-She Yang, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Seifedine Kadry, Imran Razzak
Summary: A multi-objective flower pollination algorithm is proposed in this study to solve the EEG signal denoising problem using wavelet transform. The algorithm optimizes the denoising parameters based on two measurement criteria, minimum mean squared error and maximum signal-to-noise ratio. Experimental results show that the proposed method achieves good performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Bilal H. Abed-alguni, Noor Aldeen Alawad, Mohammed Azmi Al-Betar, David Paul
Summary: This paper proposes improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. By introducing Opposition Based Learning (OBL), Variable Neighborhood Search (VNS), Laplace distribution, and Refraction Learning (RL), the binary SCA algorithm has been successfully improved. The experimental results show that the improved IBSCA3 algorithm performs well in terms of classification accuracy and fitness values, outperforming other algorithms.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Iyad Abu Doush, Osama Ahmad Alomari, Ammar Kamal Abasi, Sharif Naser Makhadmeh, Zaid Abdi Alkareem Alyasseri
Summary: This paper boosts the learning process of multilayer perceptron (MLP) neural network using hybrid metaheuristic optimization algorithms. Six versions of memetic algorithms (MAs) replace the gradient descent learning mechanism of MLP, and adaptive beta-hill climbing (A beta HC) is hybridized with six population-based metaheuristics. The results show that the proposed MA versions excel the original algorithms, with hybrid grey wolf optimization (HGWO) outperforming all other MA versions.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Environmental
Fadl A. Essa, Mohamed Abd Elaziz, Mohammed Azmi Al-Betar, Ammar H. Elsheikh
Summary: The study investigated the performance of a reverse osmosis unit integrated with a recovery energy system, under different operating system pressures and recovery ratios. A hybrid machine learning model using LSTM neural network optimized by AHA was developed to predict permeate flow and power saving of the reverse osmosis unit. The optimized model showed significantly improved prediction accuracy compared to the pure model, with high coefficient of determination values.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2023)
Article
Engineering, Multidisciplinary
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Sharif Naser Makhadmeh, Iyad Abu Doush, Raed Abu Zitar, Samah Alshathri, Mohamed Abd Elaziz
Summary: This paper proposes a hybridized version of the Harris Hawks Optimizer (HHO) with adaptive-hill-climbing optimizer for solving economic load dispatch (ELD) problems. The proposed method achieves significant performance in various ELD cases and can be considered as an efficient alternative for solving ELD problems.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Malik Sh. Braik, Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Abdelaziz I. Hammouri, Raed Abu Zitar
Summary: An Enhanced Chameleon Swarm Algorithm (ECSA) is proposed to solve non-convex Economic Load Dispatch (ELD) problems by integrating roulette wheel selection and Levy flight methods. The performance of ECSA is shown to outperform other methods on complex benchmark functions.
APPLIED INTELLIGENCE
(2023)
Review
Computer Science, Interdisciplinary Applications
Sharif Naser Makhadmeh, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, Mohammed A. Awadallah, Iyad Abu Doush, Zaid Abdi Alkareem Alyasseri, Osama Ahmad Alomari
Summary: This paper reviews and summarizes the studies that utilize the butterfly optimization algorithm (BOA) for optimization problems. It introduces the basic concepts, inspiration, and mathematical model of BOA, and categorizes the studies into different adaptation forms. The advantages, drawbacks, and future directions of BOA in dealing with optimization problems are analyzed and summarized.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Geosciences, Multidisciplinary
Muhammad Nabih, Ashraf Ghoneimi, Ahmed Bakry, Samia Allaoua Chelloug, Mohammed Azmi Al-Betar, Mohamed Abd Elaziz
Summary: This study aims to predict Poisson's ratio using ordinary well log and seismic data through machine learning algorithms. The Wild Geese Algorithm is used to determine the best configuration, enhancing the prediction process. Rock physics templates are used to interpret lithology and pore-fluid.
MARINE AND PETROLEUM GEOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Majdi Mafarja, Thaer Thaher, Mohammed Azmi Al-Betar, Jingwei Too, Mohammed A. Awadallah, Iyad Abu Doush, Hamza Turabieh
Summary: Software Fault Prediction (SFP) is an important process to detect faulty components of software early in the development life cycle. This paper proposes a machine learning framework for SFP, comparing the performance of seven classifiers and improving the results through dimensionality reduction and optimization strategies.
APPLIED INTELLIGENCE
(2023)
Review
Computer Science, Interdisciplinary Applications
Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Sharif Naser Makhadmeh, Zaid Abdi Alkareem Alyasseri, Ghazi Al-Naymat, Seyedali Mirjalili
Summary: The Marine Predators Algorithm (MPA) is a nature-inspired optimizer based on the foraging mechanisms of ocean predators. It has become popular for its derivative-free, parameterless, and easy-to-use features, leading to its wide application in various optimization problems. This review paper analyzes the growth and performance of MPA based on 102 research papers. It discusses the inspirations and theoretical concepts of MPA, focusing on its convergence behavior. The review also examines the versions of MPA proposed to improve its performance on real-world optimization problems and explores the diverse optimization applications using MPA as the main solver.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Review
Computer Science, Interdisciplinary Applications
Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Iyad Abu Doush, Sharif Naser Makhadmeh, Ghazi Al-Naymat
Summary: This paper reviews the latest versions and applications of sparrow search algorithm (SSA), a rapidly growing swarm-based algorithm proposed in 2020. SSA is inspired by the foraging behavior of sparrows. It has been widely used for optimization problems in various research fields. The paper highlights the growth of SSA, its theoretical features, and discusses the different extended versions to overcome premature convergence and enhance diversity. It also presents multi-objective SSA and analyzes the research gaps in the convergence behavior of SSA.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Materials Science, Multidisciplinary
Ghazi S. Alsoruji, A. M. Sadoun, Mohamed Abd Elaziz, Mohammed Azmi Al-Betar, A. W. Abdallah, A. Fathy
Summary: This study presents a machine learning model based on long-short term memory model and beluga whale optimizer to predict the mechanical properties of ultrafine grain Al-TiO2 nanocomposites. The model shows excellent accuracy in predicting the yield and ultimate strengths, elongation, and hardness of the composites tested.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2023)
Article
Computer Science, Artificial Intelligence
Iyad Abu Doush, Khalid Sultan, Mohammed Azmi Al-Betar, Zainab Almeraj, Zaid Abdi Alkareem Alyasseri, Mohammed A. Awadallah
Summary: This study aims to identify performance criteria for comparing automatic web accessibility evaluation tools (WAET) and determine the possibility of automatically testing SC based on current technologies. It also explores ways to reduce mistakenly reported errors through WAET. WCAG 2.1 SC level-A, AA, and AAA were analyzed, and the results can guide developers to enhance their tools by utilizing cutting-edge technologies, as well as provide performance indicators for measuring WAET's performance.
CCF TRANSACTIONS ON PERVASIVE COMPUTING AND INTERACTION
(2023)
Article
Engineering, Biomedical
Malik Shehadeh Braik, Abdelaziz I. Hammouri, Mohammed A. Awadallah, Mohammed Azmi Al-Betar, Khalaf Khtatneh
Summary: This paper presents a hybrid model for feature selection, using an improved swarm algorithm and k-nearest neighbor classifier to select optimal feature subsets. The proposed method demonstrates superior classification performance compared to other methods, indicating its potential in exploring the feature space and identifying the most useful features for classification tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Information Systems
Mohammed Azmi Al-Betar, Iyad Abu Doush, Sharif Naser Makhadmeh, Ghazi Al-Naymat, Osama Ahmad Alomari, Mohammed A. Awadallah
Summary: This survey paper comprehensively analyzes the performance and applications of Equilibrium Optimizer (EO), comparing it with eight other well-established methods. Different versions and applications of EO are discussed, highlighting their pros and cons, and suggesting future research directions.
MULTIMEDIA TOOLS AND APPLICATIONS
(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)
