Article
Computer Science, Artificial Intelligence
Quan Minh Phan, Ngoc Hoang Luong
Summary: Neural Architecture Search (NAS) is a multi-objective optimization problem that automates the design process of high-performing neural network architectures. This article introduces a local search method, PSI, to enhance the performance of MOEAs. Experimental results confirm the effectiveness of the proposed method, reducing computational costs significantly.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Sukanta Nama, Apu Kumar Saha, Sanjoy Chakraborty, Amir H. Gandomi, Laith Abualigah
Summary: A new ensemble algorithm called e-mPSOBSA, which combines the advantages of PSO and BSA, is proposed to address real-world global optimization challenges. By balancing exploitation and exploration during the search process, this algorithm achieves better performance.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Yiying Zhang
Summary: This paper proposes an improved version of backtracking search algorithm called GMPBSA, which introduces generalized mean positions and a comprehensive learning mechanism to enhance the global search ability of BSA. Experimental results show the great potential of GMPBSA in solving challenging multimodal optimization problems.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Zhengzhong Qiu, Wei Bi, Dong Xu, Hua Guo, Hongwei Ge, Yanchun Liang, Heow Pueh Lee, Chunguo Wu
Summary: This article presents a method called Efficient Self-learning Evolutionary Neural Architecture Search (ESE-NAS) that overcomes the challenges of traditional evolutionary algorithms. It uses an adaptive learning strategy and a performance predictor to guide the search process and improve efficiency.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yameng Peng, Andy Song, Vic Ciesielski, Haytham M. M. Fayek, Xiaojun Chang
Summary: Neural architecture search (NAS) automates architecture engineering in neural networks, but evaluating candidate networks is computationally expensive. To reduce this overhead, a predictor-assisted evolutionary NAS (PRE-NAS) strategy is proposed, which can perform well even with a small number of evaluated architectures. PRE-NAS leverages evolutionary search strategies and weight inheritance over generations to improve accuracy of predictions. Experimental results show that PRE-NAS outperforms state-of-the-art NAS methods, finding competitive architectures with low test error rates on CIFAR-10 and ImageNet.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Automation & Control Systems
Huangke Chen, Ran Cheng, Witold Pedrycz, Yaochu Jin
Summary: This paper proposes a method to solve multiobjective optimization problems through multi-stage evolutionary search, highlighting convergence and diversity in different search stages. The algorithm balances and addresses the issues in multiobjective optimization through two stages.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Hardware & Architecture
Luciano S. de Souza, Jonathan H. A. de Carvalho, Tiago A. E. Ferreira
Summary: This article proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. By using quantum walk as a search algorithm in a complete graph, the procedure can find all synaptic weights of the neural network. With the advantages of knowing the required number of iterations in advance and avoiding getting stuck in local minimums, this method offers an alternative to the backpropagation algorithm.
IEEE TRANSACTIONS ON COMPUTERS
(2022)
Article
Computer Science, Artificial Intelligence
Shangshang Yang, Ye Tian, Cheng He, Xingyi Zhang, Kay Chen Tan, Yaochu Jin
Summary: This article proposes a gradient-guided evolutionary approach to train neural networks, which combines the advantages of gradient-based methods and evolutionary algorithms, and considers the sparsity of the network, demonstrating its effectiveness in solving large-scale optimization problems.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Energy & Fuels
Yuxiao Zhu, Daniel W. Newbrook, Peng Dai, C. H. Kees de Groot, Ruomeng Huang
Summary: This study demonstrates the application of artificial neural network, a deep learning technique, in forward modeling the maximum power generation and efficiency of a thermoelectric generator for the first time. The neural networks, with the coupling of genetic algorithm, can optimize the geometrical structure of the generator quickly and accurately, providing a new and cost-effective approach for system level design and optimization of thermoelectric generators and other energy harvesting technologies.
Article
Computer Science, Artificial Intelligence
Yangyang Li, Ruijiao Liu, Xiaobin Hao, Ronghua Shang, Peixiang Zhao, Licheng Jiao
Summary: This paper introduces a neural network model called Quantum Neural Network (QNN) based on the principles of quantum mechanics, and proposes a neural architecture search method called EQNAS to improve QNN. Through experiments on the searched Quantum Neural Networks, the feasibility and effectiveness of the proposed algorithm in this paper are proven.
Article
Computer Science, Artificial Intelligence
Cheng He, Hao Tan, Shihua Huang, Ran Cheng
Summary: Researchers have proposed an effective evolutionary neural architecture search method that achieves state-of-the-art results by using a tailored crossover operator to help offspring architectures inherit from parent architectures.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Interdisciplinary Applications
Hamid Reza Rafat Zaman, Farhad Soleimanian Gharehchopogh
Summary: This paper introduces the advantages and disadvantages of particle swarm optimization (PSO) and backtracking search optimization algorithm (BSA), proposes an improved algorithm PSOBSA to address the issues of PSO algorithm, and validates its superior performance through experiments.
ENGINEERING WITH COMPUTERS
(2022)
Article
Computer Science, Artificial Intelligence
Malik Braik, Hussein Al-Zoubi, Heba Al-Hiary
Summary: This research focuses on updating the weights of artificial neural networks using bio-inspired algorithms such as PSO, GOA, and GWO, for identifying specific architectures in nonlinear prediction systems. The developed models were compared with traditional and state-of-the-art models, showing the efficacy of the proposed approaches in modeling.
Article
Computer Science, Artificial Intelligence
Haoyu Zhang, Yaochu Jin, Ran Cheng, Kuangrong Hao
Summary: The article proposes a computationally efficient framework for evolutionary search of convolutional networks based on a directed acyclic graph, reducing the computational costs of training deep neural networks by using random sampling of parent nodes and a node inheritance strategy, as well as introducing a channel attention mechanism in the search space to enhance feature processing capability. Experimental results show that the algorithm is competitive in terms of computational efficiency and learning performance.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Mohammadreza Chamanbaz, Roland Bouffanais
Summary: This article introduces two efficient algorithms for solving mixed-integer optimization problems. These algorithms test the feasibility of a given test solution and identify the violated constraints at each iteration step. Then, an optimization problem is constructed with the current basis and constraints related to the violating samples. The results show that both algorithms converge to the optimal solution in finite time. Algorithm 2, which features a neural network classifier, significantly improves computational performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Fang Zhu, Debao Chen, Feng Zou
Summary: A dynamic fireworks algorithm with particle swarm optimization (DFWPSO) is proposed in this paper to enhance the global performance of FWA by dynamically adjusting explosion amplitude and implementing a new update mechanism; experimental results demonstrate the competitive and effective performance of the algorithm in solving optimization problems.
Article
Computer Science, Artificial Intelligence
Feng Zou, Debao Chen, Qingzheng Xu, Ziqi Jiang, Jiahui Kang
Summary: In this paper, a two-stage personalized recommendation algorithm based on improved collaborative filtering and multi-objective optimization is proposed, aiming to enhance the accuracy and diversity of recommendations. Experimental results demonstrate the effectiveness and efficiency of the algorithm in personalized recommender systems.
Article
Computer Science, Artificial Intelligence
Yu Deng, Debao Chen, Feng Zou, Yuan Chen, Ying Zheng, Minglan Fu, Chun Wang
Summary: The paper proposes a framework of heterogeneous ensemble algorithms (EHA) that integrates multiple optimization methods with different structures. The EHA framework effectively utilizes the advantages of different algorithms without significantly increasing computational complexity. Evaluation results indicate that EHA has excellent optimization performance.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Electrical & Electronic
Yujie Wan, Minglan Fu, Lvqiang Chen, Debao Chen, Jiekun Li, Wei Zhou, Mengxue Liu
Summary: The task allocation process in the Witkey mode requires reaching a stable Nash equilibrium and achieving high total system revenue. This study proposes an incentive measure based on integral ranking to improve user participation.
ADVANCES IN MULTIMEDIA
(2022)
Correction
Computer Science, Artificial Intelligence
Yu Deng, Debao Chen, Feng Zou, Yuan Chen, Ying Zheng, Minglan Fu, Chun Wang
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Debao Chen, Yuanyuan Ge, Yujie Wan, Yu Deng, Yuan Chen, Feng Zou
Summary: This paper introduces a novel algorithm called Poplar Optimization Algorithm (POA) to solve continuous optimization problems by mimicking the sexual and asexual propagation mechanism of poplar. The algorithm shows competitive and superior performance in performance testing and successfully finds the optimal threshold for image segmentation.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Yuanyuan Ge, Debao Chen, Feng Zou, MingLan Fu, Fangzhen Ge
Summary: The competitive swarm optimizer (CSO) is an efficient algorithm for solving larger-scale multiobjective optimization problems (LSMOPs). In this study, an adaptive competitive swarm optimizer with inverse modeling is proposed to improve the performance of CSO. The winners are updated using inverse modeling and an adjacent individual competition method is introduced to enhance the distribution of solutions. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms on benchmark optimization problems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Siyu Cao, Feng Zou, Debao Chen, Hui Liu, Xuying Ji, Yan Zhang
Summary: Dynamic multi-objective problems are prevalent in daily life and practical applications. This paper proposes a new hybrid prediction model (HPM) to solve these problems, and the results show that HPM outperforms other strategies in dynamic optimization.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ziqi Jiang, Feng Zou, Debao Chen, Siyu Cao, Hui Liu, Wei Guo
Summary: This paper proposes a new ensemble multi-swarm method based on teaching-learning-based optimization (EMTLBO), which integrates multiple algorithms to achieve better optimization performance. It introduces an evaluating mechanism and an algorithm matching mechanism to improve the overall optimization performance. The experimental results demonstrate the feasibility and effectiveness of EMTLBO, and it also shows good performance in image segmentation.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Feng Zou, Debao Chen, Hui Liu, Siyu Cao, Xuying Ji, Yan Zhang
Summary: Fitness landscape analysis (FLA), as a powerful analytical tool, has been widely applied in various optimization areas. It helps to gain a deep understanding of the characteristics of complex optimization problems and improve algorithm performance on specific problems.
Proceedings Paper
Computer Science, Theory & Methods
Xuying Ji, Feng Zou, Debao Chen, Yan Zhang
Summary: Fitness landscape is an evolutionary mechanism that can improve optimization performance by analyzing the fitness landscape. This paper introduces a new fitness-landscape-driven particle swarm optimization algorithm, characterizing the fitness landscape to improve optimization performance and introducing a selection mechanism for choosing better variants. Experimental results show that the proposed algorithm significantly improves optimization accuracy and convergence.
INTELLIGENT COMPUTING THEORIES AND APPLICATION (ICIC 2022), PT I
(2022)
Article
Engineering, Electrical & Electronic
Tao Sheng, Shengzhe Shi, Yuanyang Zhu, Debao Chen, Sheng Liu
Summary: This study presents a fast and accurate method for measuring milk composition using an infrared spectral sensor and machine learning algorithm. The results show that the proposed system can provide real-time, simple, and fast determination of milk protein and fat content.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Multidisciplinary Sciences
Yuan Chen, Debao Chen, Yu Deng, Feng Zou, Ying Zheng, Minglan Fu, Chun Wang
Summary: The study presents an information feedback model based on mutual information to directly handle multi-objective optimization problems, which can be easily integrated with any optimization algorithm, improving the balance between convergence and diversity in the population.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)