Article
Computer Science, Artificial Intelligence
Changting Zhong, Gang Li, Zeng Meng
Summary: This paper presents a novel swarm-based metaheuristic algorithm called beluga whale optimization (BWO), which is inspired by the behaviors of beluga whales, for solving optimization problems. BWO consists of three phases: exploration, exploitation, and whale fall, corresponding to pair swim, prey, and whale fall behaviors, respectively. The self-adaptive balance factor and probability of whale fall in BWO play significant roles in controlling the exploration and exploitation capabilities. Additionally, Levy flight is introduced to enhance the global convergence in the exploitation phase. The effectiveness of BWO is evaluated using 30 benchmark functions and compared with 15 other metaheuristic algorithms through qualitative, quantitative, and scalability analysis. The results show that BWO is a competitive algorithm for solving unimodal and multimodal optimization problems. Furthermore, BWO achieves the first overall rank in the scalability analysis of benchmark functions among the compared metaheuristic algorithms. Four engineering problems are also solved to demonstrate the merits and potential of BWO in solving complex real-world optimization problems. The source code of BWO is publicly available.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Amir Seyyedabbasi, Farzad Kiani
Summary: The study introduces a new metaheuristic algorithm, SCSO, which mimics the behavior of sand cats. The algorithm performs well in finding good solutions and outperforms compared methods in various test functions and engineering design problems.
ENGINEERING WITH COMPUTERS
(2023)
Article
Computer Science, Artificial Intelligence
Mohamed Abdel-Basset, Reda Mohamed, Mohammed Jameel, Mohamed Abouhawwash
Summary: This work presents a novel metaheuristic algorithm called Nutcracker Optimization Algorithm (NOA), inspired by the behaviors of Clark's nutcrackers. NOA mimics the nutcracker's search for seeds and cache storage during summer and fall, as well as its spatial memory strategy during winter and spring. The algorithm is evaluated and compared with other optimization algorithms, demonstrating superior results and ranking first among all methods.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Shijie Zhao, Tianran Zhang, Shilin Ma, Mengchen Wang
Summary: This paper proposes a novel swarm intelligence-based metaheuristic called sea-horse optimizer (SHO) which mimics the movement, predation, and breeding behaviors of sea horses in nature. The algorithm is designed to balance local exploitation and global exploration, and has been shown to be a high-performance optimizer with positive adaptability to deal with constraint problems.
APPLIED INTELLIGENCE
(2023)
Article
Engineering, Multidisciplinary
Hoda Zamani, Mohammad H. Nadimi-Shahraki, Amir H. Gandomi
Summary: This paper presents a novel bio-inspired algorithm called SMO, which mimics the behaviors of starlings during their stunning murmuration, to solve complex engineering optimization problems. The SMO introduces dynamic multi-flock construction and three new search strategies, achieving competitive results in solution quality and convergence rate compared to other state-of-the-art algorithms.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Zheping Yan, Jinzhong Zhang, Jia Zeng, Jialing Tang
Summary: The enhanced whale optimization algorithm adopts the Levy flight strategy and ranking-based mutation operator to overcome the drawbacks of the basic algorithm, achieving a balanced exploration and exploitation to improve search performance.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Benyamin Abdollahzadeh, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: Metaheuristics, especially the African Vultures Optimization Algorithm (AVOA), play a crucial role in solving optimization problems, outperforming existing algorithms in standard benchmarks and engineering design problems. The statistical evaluation further confirms the significant superiority of AVOA.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Gaurav Dhiman
Summary: The SSC algorithm combines sine-cosine functions and attacking strategy of SHO algorithm to find optimal solutions for complex problems, demonstrating robustness, effectiveness, efficiency, and convergence analysis in comparison with other competitor approaches.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Jiaze Tu, Huiling Chen, Jiacong Liu, Ali Asghar Heidari, Xiaoqin Zhang, Mingjing Wang, Rukhsana Ruby, Quoc-Viet Pham
Summary: The study introduces an enhanced WOA method, EWOA, which combines a new communication mechanism and partial utilization of the BBO algorithm to improve the exploration ability, exploitation ability, and convergence speed of the algorithm.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Interdisciplinary Applications
Ailiang Qi, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Huiling Chen, Lei Xiao
Summary: A new variant of the whale optimization algorithm, named LXMWOA, is proposed in this paper to enhance the performance of WOA by introducing Levy initialization strategy, directional crossover mechanism, and directional mutation mechanism. Experimental results show that LXMWOA outperforms its peers in both exploration and exploitation capabilities, suggesting great potential for solving engineering problems.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Farid MiarNaeimi, Gholamreza Azizyan, Mohsen Rashki
Summary: This paper introduces a new meta-heuristic algorithm called Horse Herd Optimization Algorithm (HOA), inspired by horses' behavior, which shows excellent performance in high-dimensional optimization problems. By imitating the behavior features of horses at different ages, HOA has a large number of control parameters leading to efficient solving of complex problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ryan Solgi, Hugo A. Loaiciga
Summary: This study evaluates the performance of seven bee-inspired metaheuristic algorithms in solving continuous optimization problems, ranks them based on convergence efficiency, and identifies ABC, BEGA, and MBO as the most efficient algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Interdisciplinary Applications
Abdolkarim Mohammadi-Balani, Mahmoud Dehghan Nayeri, Adel Azar, Mohammadreza Taghizadeh-Yazdi
Summary: This paper introduces a nature-inspired global optimization algorithm, GEO, based on the hunting behavior of golden eagles, and a multi-objective algorithm, MOGEO. Testing on benchmark functions and multi-objective benchmark functions show that GEO and MOGEO outperform other algorithms in optimization performance.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Automation & Control Systems
Shijie Zhao, Tianran Zhang, Shilin Ma, Miao Chen
Summary: This paper proposes a novel swarm intelligence bioinspired optimization algorithm, called the Dandelion Optimizer (DO), that simulates the process of dandelion seed long-distance flight for solving continuous optimization problems. Experimental results indicate that the DO method has outstanding iterative optimization and strong robustness.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Weiguo Zhao, Liying Wang, Zhenxing Zhang, Honggang Fan, Jiajie Zhang, Seyedali Mirjalili, Nima Khodadadi, Qingjiao Cao
Summary: The electric eel foraging optimization (EEFO) algorithm is a swarm-based, bio-inspired metaheuristic algorithm that imitates the foraging behaviors of electric eels. Through mathematical modeling, EEFO provides both exploration and exploitation abilities during the optimization process. Experimental results show that EEFO outperforms other algorithms in various tests, especially in optimization problems with unimodal characteristics and many constraints and variables.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Energy & Fuels
Xuemeng Weng, Yun Liu, Ali Asghar Heidari, Zhennao Cai, Haiping Lin, Huiling Chen, Guoxi Liang, Abdulmajeed Alsufyani, Sami Bourouis
Summary: The determination of photovoltaic parameters is crucial for solar system operation and energy management. This study proposes a new optimization algorithm TLBOBSA to accurately simulate PV models, and its advantages in parameter extraction are verified through experiments.
ENERGY SCIENCE & ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Shubiao Wu, Ali Asghar Heidari, Siyang Zhang, Fangjun Kuang, Huiling Chen
Summary: This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to improve the shortcomings of the original SMA algorithm. The GBSMA algorithm introduces a Gaussian function for faster convergence and expands the search space, and also uses a differential evolution update strategy for better global search performance. Experimental results show that GBSMA outperforms the original SMA and other similar algorithms in terms of convergence speed and solution accuracy.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Review
Automation & Control Systems
Huiling Chen, Chenyang Li, Majdi Mafarja, Ali Asghar Heidari, Yi Chen, Zhennao Cai
Summary: This paper provides a comprehensive review of critical studies related to the development of Slime Mould Algorithm (SMA), including an analysis of advanced versions of SMA and its application domains. The survey shows that SMA outperforms established metaheuristics in terms of speed and accuracy, and suggests possible future research directions.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Shengsheng Wang, Bilin Wang, Zhe Zhang, Ali Asghar Heidari, Huiling Chen
Summary: This paper proposes a novel OT-based Class-Aware Sample Reweighting (CASR) method to achieve sample-level fine-grained alignment between multi-source and target. Extensive experiments show that CASR presents significant advantages compared with other MSDA methods, and the visualization analysis further demonstrates the effectiveness of each proposed module.
Article
Computer Science, Artificial Intelligence
Xinsen Zhou, Wenyong Gui, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen
Summary: Continuous ant colony optimization algorithm incorporates a random following strategy to enhance global optimization performance and effectively handle high-dimensional feature selection problems. The algorithm performs competitively with other state-of-the-art algorithms in benchmark tests and outperforms well-known classification methods on high-dimensional datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Pharmacology & Pharmacy
Huiling Chen, Chenchen Liu, Meng Li, Yida Zhang, Zhendong Wang, Qiyao Jiang, Jianxin Wang, Qi Wang, Yue Zhuo
Summary: A quantitative method was established to investigate the correlation between DIOB RM-protein adducts (DRPAs) and hepatotoxicity. It was found that the severity of hepatotoxicity positively correlated with the content of DRPAs. Furthermore, the combination of DIOB with ferulic acid (FA) was proven to reduce the production of DRPAs, decrease serum ALT/AST levels, and ameliorate DIOB-induced liver injury.
DRUG METABOLISM AND PHARMACOKINETICS
(2023)
Article
Computer Science, Information Systems
Lejun Zhang, Yuan Li, Ran Guo, Guopeng Wang, Jing Qiu, Shen Su, Yuan Liu, Guangxia Xu, Huiling Chen, Zhihong Tian
Summary: With the development of blockchain technology, smart contracts have gained attention for their ability to reduce trust costs compared to traditional contracts. However, they also face the risk of being hacked, making research on smart contract vulnerability detection crucial. We proposed a novel smart contract reentrancy vulnerability detection model based on BiGAS, which achieved a high accuracy and F1-score of over 93% in detecting reentrancy vulnerabilities. Our method outperformed advanced methods by an improvement range of 4% to 23%, making it significantly better in detecting smart contract reentrancy vulnerabilities.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2023)
Article
Mathematics, Applied
Huiling Chen, Chunmei Zhang, Yuli Feng, Qin Xu
Summary: This article aims to identify the partial topological structures of delayed complex networks using the drive-response concept. By combining graph-theoretic method and adaptive synchronization, the identification criteria for stochastic multi-weighted complex networks with or without time delays are obtained and the effectiveness of the proposed theoretical results is validated through numerical simulations.
ADVANCES IN APPLIED MATHEMATICS AND MECHANICS
(2023)
Article
Computer Science, Artificial Intelligence
Maofa Wang, Qizhou Gong, Huiling Chen, Guangda Gao
Summary: Developing a smart analytics system to accurately diagnose diabetic retinopathy is crucial. This research proposes a new deep transfer network framework that utilizes an optimized Fruit Fly Optimization Algorithm (MALBFOA) to diagnose diabetic retinopathy using color fundus photography as input. The experimental results show that the proposed framework outperforms other benchmark models in terms of convergence rate and accuracy.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Meilin Zhang, Huiling Chen, Ali Asghar Heidari, Zhennao Cai, Nojood O. Aljehane, Romany F. Mansour
Summary: The recently proposed swarm intelligence algorithm, Runge-Kutta Optimization (RUN), is rooted in the fourth-order Runge-Kutta method. Compared with its counterparts, RUN has a more concrete theoretical foundation and more powerful optimization efficacy. However, it suffers from shortcomings in exploration ability and imbalance between exploration and exploitation. An improved version, OCRUN, based on opposition-based learning and cuckoo search, is proposed to overcome these deficiencies. OCRUN exhibits excellent performance in test functions and parameter sensitivity analysis experiments, and it also performs well in feature selection cases.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Hardware & Architecture
Jiaochen Chen, Zhennao Cai, Ali Asghar Heidari, Lei Liu, Huiling Chen, Jingye Pan
Summary: This paper proposes a dynamic mechanism-assisted ABC algorithm (EABC) that improves the convergence speed and optimization performance of the traditional ABC algorithm. The EABC-based MTIS model achieves effective results in COVID-19 X-ray chest image segmentation.
Article
Biochemical Research Methods
Hui Fang, Cheng Zhong, Jiaman Sun, Huiling Chen
Summary: Fusarium wilt of banana caused by Fusarium oxysporum f. sp. cubense is a devastating fungal disease in the banana industry. This study analyzed the proteomic data of resistant and susceptible banana varieties to Foc4, revealing differences in protein accumulation profiles and related functional modules. Understanding the resistance mechanism and identifying resistance-related genes in banana variety improvement are of great significance.
JOURNAL OF PROTEOMICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Yunlou Qian, Jiaqing Tu, Gang Luo, Ce Sha, Ali Asghar Heidari, Huiling Chen
Summary: This paper investigates the application of remote sensing images in urban surface morphology and geographic conditions, using the multi-threshold image segmentation method for image segmentation research. The performance of the original algorithm is enhanced by introducing salp foraging behavior. The experimental results demonstrate the advantages of SSACO in remote sensing image segmentation.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Jie Xing, Qinqin Zhao, Huiling Chen, Yili Zhang, Feng Zhou, Hanli Zhao
Summary: This paper proposes a bee foraging behavior-driven mutational salp swarm algorithm (BMSSA) based on an improved bee foraging strategy and an unscented mutation strategy. Experimental results validate the effectiveness of BMSSA in optimization problems and feature selection tasks.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Automation & Control Systems
Chunmei Zhang, Huiling Chen, Qin Xu, Yuli Feng, Ran Li
Summary: This article discusses a class of stochastic hybrid delayed coupled systems with multiple weights, and derives several conditions for asymptotic synchronization and topology identification of the systems based on Kirchhoff's Matrix-Tree Theorem and Lyapunov stability theory.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2024)
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)
