Article
Computer Science, Artificial Intelligence
Maryam T. Abdulkhaleq, Tarik A. Rashid, Abeer Alsadoon, Bryar A. Hassan, Mokhtar Mohammadi, Jaza M. Abdullah, Amit Chhabra, Sazan L. Ali, Rawshan N. Othman, Hadil A. Hasan, Sara Azad, Naz A. Mahmood, Sivan S. Abdalrahman, Hezha O. Rasul, Nebojsa Bacanin, S. Vimal
Summary: Harmony Search (HS) is a popular metaheuristic algorithm known for its ability to find solutions to optimization problems. It is widely applied in various fields, including healthcare systems, engineering, and computer science, due to its balanced exploratory and convergence behavior. This review paper provides a comprehensive examination of the current studies and applications of HS in healthcare systems, proposing an operational framework and serving as a valuable resource for prospective scholars.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Malik Braik, Alaa Sheta, Heba Al-Hiary
Summary: The study introduces a novel nature-inspired search optimization algorithm called Capuchin Search Algorithm (CapSA), which is designed based on the foraging behaviors of capuchin monkeys in forests to solve global optimization problems efficiently.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Mohamed Abd Elaziz, Putra Sumari, Zong Woo Geem, Amir H. Gandomi
Summary: The paper introduces a novel nature-inspired meta-heuristic optimizer, RSA, based on the hunting behavior of crocodiles. Through implementing two main steps of crocodile behavior, RSA shows unique search methods compared to existing algorithms, and achieves better results in various test functions and engineering problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Engineering, Chemical
Srinivas Soumitri Miriyala, Keerthi NagaSree Pujari, Sakshi Naik, Kishalay Mitra
Summary: This paper proposes an alternative model using Artificial Neural Networks to optimize the crystallization process, achieving a significant speed improvement while maintaining accuracy through a neural architecture search strategy for hyperparameter tuning.
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, Information Systems
Gianni D'Angelo, Francesco Palmieri
Summary: Genetic algorithms have shown effectiveness in solving real-world optimization problems, especially when combined with gradient-descent technique. The hybrid algorithm proposed in this work aims to improve the efficiency of GAs in finding global optimal solutions by utilizing the gradient-descent capability for local searching. Experimental results demonstrate competitiveness in solution precision and resource efficiency compared to other complex approaches.
INFORMATION SCIENCES
(2021)
Article
Engineering, Civil
Mahmoud Jahjouh, Semih Erhan
Summary: This research aims to obtain the optimal shape of precast I girders commonly used in bridge construction by utilizing optimization algorithms, and the resulting new girder sections exhibit better structural efficiency than current standards.
Article
Computer Science, Artificial Intelligence
Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, Yong Liu
Summary: Dynamic multiobjective optimization problem refers to a multiobjective optimization problem that varies over time. To solve this kind of problem, evolutionary search with prediction approaches have been developed to estimate the changes in the problem. However, existing prediction methods only focus on the change in the decision space. In this article, a new approach is proposed that conducts prediction from both the decision and objective spaces. Experimental results show the effectiveness of the proposed method in solving both benchmark and real-world DMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Operations Research & Management Science
Ran Etgar, Yuval Cohen
Summary: This paper proposes a statistical-based methodology to balance the risk of missing a better solution and expected computing effort when assisting search technique optimizers in making informed decisions about terminating the heuristic search process. The methodology can serve as a general tool for various meta-heuristic studies and lay a foundation for further research on improving search termination criteria.
Article
Computer Science, Artificial Intelligence
John Pillans
Summary: Design automation involves a trade-off between using expert knowledge to restrict possible solutions or spending time searching through solutions. This paper introduces an evolutionary search method for finding circuit topologies and component values, showing its effectiveness in simple problems and comparing the efficiency of different evolutionary search techniques. The proposed hybrid evolutionary method is found to be more efficient under certain conditions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Interdisciplinary Applications
Mahdi Hasanipanah, Behrooz Keshtegar, Duc-Kien Thai, Nguyen-Thoi Troung
Summary: A novel hybrid artificial neural network (ANN) model based on adaptive musical inspired optimization method is proposed for accurate prediction of blast-induced flyrock. The model showed better predictive performance compared to other models.
ENGINEERING WITH COMPUTERS
(2022)
Review
Chemistry, Multidisciplinary
Zitong Wang, Yan Pei, Jianqiang Li
Summary: The multi-objective optimization problem is challenging due to conflicts among various objectives and functions. The research and application of multi-objective evolutionary algorithms (MOEA) have made significant progress in solving such problems. This survey provides a comprehensive investigation of MOEA algorithms, classifies them by evolutionary mechanism, and suggests the combination of chaotic evolution algorithm with representative search strategies for improving the search capability of MOEAs.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Laith Abualigah, Mohamed Abd Elaziz, Abdelazim G. Hussien, Bisan Alsalibi, Seyed Mohammad Jafar Jalali, Amir H. Gandomi
Summary: The Lightning Search Algorithm (LSA) is a novel meta-heuristic optimization method introduced in 2015 for solving constraint optimization problems. It focuses on improving the effectiveness of the fitness function by finding minimum or maximum costs. The applications of LSA span across benchmark functions, machine learning, network applications, engineering, and more.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Lei Zhou, Liang Feng, Abhishek Gupta, Yew-Soon Ong
Summary: The evolutionary algorithm with learning capability has attracted increasing research interests, with the Autoencoding Evolutionary Search (AEES) showing promising performance in transferring knowledge from past search experiences. In this study, a Kernelized Autoencoding Evolutionary-Search (KAES) paradigm is proposed to adaptively select linear and kernelized autoencoding methods for effective knowledge transfer across problem domains during the evolutionary search process. Comprehensive empirical studies on benchmark multiobjective optimization problems and a real-world vehicle crashworthiness design problem are conducted to validate the efficacy of KAES.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Michal Okulewicz, Mateusz Zaborski, Jacek Mandziuk
Summary: This paper introduces a new version of a hyper-heuristic framework called Generalized Self-Adapting Particle Swarm Optimization with samples archive (M-GAPSO). The framework hybridizes Particle Swarm Optimization, Differential Evolution, and model based optimizers and regulates the ratio of different algorithms within the population using an adaptation scheme. Experimental results on various benchmark functions demonstrate that M-GAPSO outperforms other optimization methods, including the basic DE algorithm.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Industrial
Ying-Ying Huang, Quan-Ke Pan, Liang Gao
Summary: This paper investigates the distributed permutation flowshop scheduling problem and proposes an effective memetic algorithm (EMA). A constructive heuristic and an initialisation method are used to generate high-quality and diverse initial populations. The EMA uses a new structure of a small iteration nested within a large iteration and includes targeted and flexible local search methods. The experimental results confirm the effectiveness and efficiency of the EMA.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Industrial
Jin Xie, Xinyu Li, Liang Gao, Lin Gui
Summary: This paper focuses on the role of neighbourhood structures in solving the job shop scheduling problem (JSP) and proposes a new N8 neighbourhood structure. Experimental results show that the N8 structure is more effective and efficient in solving JSP compared to other neighbourhood structures.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Engineering, Multidisciplinary
Guokai Liu, Weiming Shen, Liang Gao, Andrew Kusiak
Summary: This paper proposes an active federated transfer algorithm based on broad learning to address the dynamic domain-shift issue in federated learning. The algorithm dispatches a global model to the source clients for collaborative modeling, initializes the global model with a federated averaging strategy, annotates emerging signals from the target clients using an active sampling strategy, and adapts the global model to the target domain through an asynchronous update scheme. Computational results validate the superior accuracy and efficiency of the proposed algorithm.
Article
Physics, Fluids & Plasmas
C. Shen, W. Zheng, Y. Ding, X. Ai, F. Xue, Y. Zhong, N. Wang, L. Gao, Z. Chen, Z. Yang, Y. Pan
Summary: This paper introduces an interpretable disruption predictor based on physics-guided feature extraction (IDP-PGFE) and presents its results on J-TEXT experiment data. Compared to models using raw signal input, IDP-PGFE with physics-guided features effectively improves prediction performance. The model's high performance ensures the validity of interpretation results and provides insights into the mechanism of disruption in J-TEXT.
Article
Automation & Control Systems
Jiaxin Fan, Yingli Li, Jin Xie, Chunjiang Zhang, Weiming Shen, Liang Gao
Summary: In this article, a hybrid evolutionary algorithm using two solution representations is proposed to solve the challenging hybrid flow-shop scheduling problem. Experimental results show that the proposed method outperforms other algorithms and finds new best solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Thermodynamics
Qixuan Zhong, Parthiv K. Chandra, Wei Li, Liang Gao, Akhil Garg, Song Lv, K. Tai
Summary: This article focuses on the problem of fluctuating cooling system flow caused by different working states during the operation of electric vehicles. The authors propose a two-dimensional topology optimization method for obtaining cooling plates with different topological structures. The results indicate that the optimized cooling plate structure under low flow conditions has better heat dissipation performance.
APPLIED THERMAL ENGINEERING
(2024)
Article
Thermodynamics
Huanwei Xu, Lingfeng Wu, Shizhe Xiong, Wei Li, Akhil Garg, Liang Gao
Summary: The article proposes a feature selection method to enhance the accuracy of SOH prediction by removing insignificant features from the input data during data preparation. Additionally, a skip connection is added to the CNN-LSTM model to address the degradation of neural networks caused by multi-layer LSTM. Experimental results demonstrate that the feature selection approach improves SOH prediction accuracy and reduces computational load. Compared to other neural network models, the CNN-LSTM-Skip model exhibits better robustness and higher accuracy across different conditions, achieving RMSE below 0.004 on the NASA and Oxford datasets.
Article
Automation & Control Systems
Qihao Liu, Cuiyu Wang, Xinyu Li, Liang Gao
Summary: Green manufacturing is increasingly important in the global industrial field, and system integration can achieve more efficient and less energy-consuming production. This paper studies the green multi-objective integrated process planning and scheduling problem considering the logistics system, proposes a multi-population co-evolutionary algorithm, and verifies its effectiveness and superiority through experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Mechanical
Mian Zhou, Liang Gao, Mi Xiao, Xiliang Liu, Mingzhe Huang
Summary: This paper proposes a multiscale topology optimization method using Nitsche-type isogeometric analysis (IGA) for structures described by multiple non-uniform rational B-spline (NURBS) patches. The method calculates unknown structural responses at macroscale using Nitsche-type IGA, while predicting mechanical properties of microstructures at microscale using a Kriging metamodel. The proposed method is effective for design of structures described by multiple NURBS patches.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Engineering, Mechanical
Wenke Qiu, Qifu Wang, Liang Gao, Zhaohui Xia
Summary: This paper presents a novel approach to stress-based topology optimization using NURBS representations of geometric boundaries for stress minimization and stress-constrained problems. The methodology is compatible with CAD systems and uses Extended IsoGeometric Analysis (XIGA) for accurate stress field representation. A p-norm aggregation scheme and a Lagrange multiplier are employed to measure and enforce stress constraints. The proposed approach reduces computational costs and improves stability through a Lagrange multiplier trail strategy, normalization scheme, and a partial differential equation filter. Validation studies demonstrate its effectiveness and advantages over other FEA-based optimization methods in terms of computational efficiency. Overall, this paper makes a significant contribution to stress-based topology optimization in terms of accuracy, flexibility, and degree of freedom (DOF) cost.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Xin-Rui Tao, Quan-Ke Pan, Hong-Yan Sang, Liang Gao, Ao-Lei Yang, Miao Rong
Summary: This study develops a nondominated sorting genetic algorithm-II (NSGA-II) with Q-learning to address the disturbance factors in the distributed permutation flowshop problem. An iterated greedy algorithm (IG) is proposed to generate an initial solution, and the NSGA-II algorithm is designed to optimize dual-objective problems. The results confirm the high efficiency of the proposed algorithm in solving the rescheduling problem in the distributed permutation flowshop.
KNOWLEDGE-BASED SYSTEMS
(2023)
Proceedings Paper
Engineering, Electrical & Electronic
Chi Hou Chan, Liang Gao, Shangcheng Kong, Geng-Bo Wu, Kam Man Shum, Ka Fai Chan
Summary: This paper provides an overview of the research conducted at the State Key Laboratory of THz and Millimeter Waves, City University of Hong Kong, on passive and active on-chip terahertz antennas and arrays. The focus for passive antennas is on impedance and gain bandwidth performance, as well as size reduction of feeding networks for arrays. For active on-chip antennas, the emphasis is on antenna size, array placement, and impedance matching to achieve high radiation power.
2023 INTERNATIONAL WORKSHOP ON ANTENNA TECHNOLOGY, IWAT
(2023)
Article
Engineering, Mechanical
Lin Gui, Xinyu Li, Liang Gao, Cuiyu Wang
Summary: This paper explores the domain knowledge of the job-shop scheduling problem (JSP) and proposes sufficient and necessary constraint conditions to find all feasible neighbourhood solutions, allowing thorough local search. A new neighbourhood structure is designed and a fast calculation method for all feasible neighbourhood solutions is provided. Experimental results show that the calculation method is effective and the new neighbourhood structure outperforms other famous and influential structures.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Bin Wang, Long Wen, Xinyu Li, Liang Gao
Summary: This article proposes a new adaptive class center generalization network (ACCGN) to learn invariant feature representations of orientation signals from multiple source domains. ACCGN optimizes the data features from interclass and intraclass simultaneously, and has been tested on two famous bearing datasets, showing its effectiveness on the CWRU and JNU datasets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Hongjin Wu, Ruoshan Lei, Yibing Peng, Liang Gao
Summary: Machining feature recognition (MFR) is an important step in computer-aided process planning that infers manufacturing semantics from CAD models. Deep learning methods like AAGNet overcome the limitations of traditional rule-based methods by learning from data and preserving geometric and topological information with a novel representation. AAGNet outperforms other state-of-the-art methods in accuracy and complexity, showing potential as a flexible solution for MFR in CAPP.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
(2024)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
