Article
Computer Science, Artificial Intelligence
Christian L. Camacho-Villalon, Marco Dorigo, Thomas Stutzle
Summary: This paper proposes the use of automatic design to overcome the limitations of manually designing PSO algorithms. They develop a flexible software framework called PSO-X, which integrates the automatic configuration tool irace to select and configure high-performing PSO algorithms from a large number of algorithm components.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Telecommunications
Junluo Yin, Xiong Luo, Yueqin Zhu, Weiping Wang, Long Wang, Chao Huang, Jenq-Haur Wang
Summary: This paper explores the application of edge computing in geological data analysis, proposing a predictive evaluation scheme based on drilling data by combining edge computing devices and geological data analysis models. The use of Long Short-Term Memory (LSTM) and Particle Swarm Optimization algorithm is shown to enhance computational performance and mineral exploration efficiency in the edge computing environment.
TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES
(2021)
Article
Computer Science, Artificial Intelligence
Diana Cristina Valencia-Rodriguez, Carlos A. Coello Coello
Summary: Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic algorithm that utilizes information exchange between particles to explore the search space. This study focuses on the influence of the number of connections among particles in Multi-Objective Particle Swarm Optimizers (MOPSOs) using random regular graphs as the swarm topology. Experimental results indicate that a higher connection degree can lead to algorithm instability in various problems, and MOPSOs with the same connection degree exhibit similar behavior.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Materials Science, Multidisciplinary
Kiyanoush Goudarzi, Moonjoo Lee
Summary: This paper describes the inverse design of a waveguide crossing using the particle swarm optimization algorithm and three-dimensional finite-difference time-domain simulation. The designed device has a small footprint, short simulation time, and excellent performance, making it suitable for semiconductor fabrication processes.
RESULTS IN PHYSICS
(2022)
Article
Multidisciplinary Sciences
Xinru Li, Zihan Lin, Haoxuan Lv, Liang Yu, Ali Asghar Heidari, Yudong Zhang, Huiling Chen, Guoxi Liang
Summary: This paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm and the Powell mechanism to overcome the limitations of the original slime mould algorithm. Experimental results demonstrate that PSMADE exhibits outstanding performance in solving complex problems and shows potential as an effective problem-solving tool.
Article
Computer Science, Artificial Intelligence
Jian Peng, Yibing Li, Hongwei Kang, Yong Shen, Xingping Sun, Qingyi Chen
Summary: This study investigates the relationship between information propagation speed and algorithm performance in particle swarm optimization, finding a strong negative correlation with population diversity in early iterations. It also highlights that the impact of population topology on optimization results is similar when solving problems with the same property.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Pham Vu Hong Son, Nghiep Trinh Nguyen Dang
Summary: The study introduces a hybrid multi-verse optimizer model (hDMVO) that combines the multi-verse optimizer (MVO) and the sine cosine algorithm (SCA) to solve the discrete time-cost trade-off problem (DTCTP). The optimality of the algorithm is evaluated using 23 benchmark test functions, demonstrating its competitiveness with other algorithms. The performance of hDMVO is further evaluated using four benchmark test problems, showing its superiority in time-cost optimization for large-scale and complex projects compared to previous algorithms.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Lei Wang, Zhengchao Liu
Summary: This paper introduces a new data-driven product design evaluation method, which establishes a multi-stage evaluation indicator model and an improved artificial neural network combined with the PSO-Adam optimization algorithm to achieve fast and accurate design evaluation. Experimental results demonstrate that the proposed method can help designers comprehensively consider design parameters and conduct effective evaluation.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Raghav Prasad Parouha, Pooja Verma
Summary: This paper introduces an advanced hybrid algorithm haDEPSO to solve optimization problems by integrating the advantages of advanced DE and PSO. The efficiency of the algorithm is verified through various test suites, demonstrating its superiority in terms of accuracy and convergence speed.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Interdisciplinary Applications
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Summary: This article introduces the application of the support vector classifier in geological modeling and proposes an improved method based on particle swarm optimization to select the best model parameters. Through the application in the modeling process of the Iju porphyry copper deposit, the effectiveness and superiority of this method are demonstrated.
COMPUTERS & GEOSCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Xueli Shen, Daniel C. Ihenacho
Summary: Particle swarm optimization and differential evolution are two nature-inspired global optimization algorithms used to simplify complex mathematical models and sensitivity methods in gas cyclone design, achieving optimal solutions by minimizing an objective function.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Multidisciplinary
Jian Zhu, Jianhua Liu, Yuxiang Chen, Xingsi Xue, Shuihua Sun
Summary: The paper introduces the Binary Restructuring Particle Swarm Optimization (BRPSO) algorithm as an adaptation of the Restructuring Particle Swarm Optimization (RPSO) algorithm for solving discrete optimization problems. Unlike other binary metaheuristic algorithms, BRPSO does not use transfer functions, instead relying on comparison results and a novel perturbation term for the particle updating process. The algorithm requires fewer parameters and exhibits high exploration capability, as demonstrated by experiments on feature selection problems.
Article
Mathematics
Alessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Riccardo Zich, Alessandro Gandelli
Summary: Reflectarray antennas are low-profile high-gain systems widely used in the aerospace industry. The design complexity and the need for high scanning capabilities have led to the development of an optimization environment that can be applied with evolutionary optimization algorithms.
Article
Thermodynamics
Amy Allen, Gregor Henze, Kyri Baker, Gregory Pavlak, Michael Murphy
Summary: This work presents a topology optimization framework for district thermal energy systems, aiming to minimize life cycle cost by selecting the best subset of buildings and network topology. The application of this framework can significantly reduce energy use intensity and achieve cost savings in the life cycle.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Serhat Kilicarslan, Emrah Donmez
Summary: This study introduces a novel approach combining adaptive particle swarm optimization and artificial bee colony algorithm to effectively classify microarray datasets for early diagnosis of cancer. The most defining features are selected through feature selection algorithms, and different classification algorithms are used for classification.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Zi-Jia Wang, Zhi-Hui Zhan, Yun Li, Sam Kwong, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a novel local search technique, named FDLS, based on individual information including fitness and distance, to execute precise local search operations on global optima in multimodal algorithms, avoiding meaningless local search operations on local optima or similar areas. The proposed FDLS technique is integrated with an adaptive differential evolution algorithm called ADE, and the experiments on the CEC2015 multimodal competition demonstrate its effectiveness and superiority compared to other multimodal algorithms.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Jian-Yu Li, Ke-Jing Du, Zhi-Hui Zhan, Hua Wang, Jun Zhang
Summary: This article proposes a novel three-layer DDE framework, along with three novel methods, for solving the resource allocation and search efficiency problems in distributed differential evolution. The effectiveness and efficiency of the framework and methods are demonstrated through theoretical analysis and extensive experiments.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xin Zhang, Bo-Wen Ding, Xin-Xin Xu, Jian-Yu Li, Zhi-Hui Zhan, Pengjiang Qian, Wei Fang, Kuei-Kuei Lai, Jun Zhang
Summary: Decomposition methods are important in CCEAs for solving large-scale optimization problems. The proposed GDD method improves the grouping accuracy by mining interactions among variables and dealing with computational roundoff errors. GDD shows better performance in grouping and fault tolerance, especially for overlapping problems.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Kun Guo, Zhanhong Chen, Xu Lin, Ling Wu, Zhi-Hui Zhan, Yuzhong Chen, Wenzhong Guo
Summary: In this paper, a novel algorithm is proposed that combines label propagation, multi-objective particle swarm optimization, and graph attention variational autoencoder to achieve community detection. The label propagation strategy is used to speed up the evolution process of the swarm, and the optimal solutions found by the optimization algorithm are embedded into the objective of the autoencoder to improve the quality of embedding vectors. Experimental results show the feasibility and effectiveness of our algorithm compared to state-of-the-art algorithms.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Information Systems
Jia-Quan Yang, Qi-Te Yang, Ke-Jing Du, Chun-Hua Chen, Hua Wang, Sang-Woon Jeon, Jun Zhang, Zhi-Hui Zhan
Summary: This article proposes a bi-directional feature fixation (BDFF) framework for particle swarm optimization (PSO) to reduce the search space in large-scale feature selection. BDFF uses two opposite search directions to guide particles to adequately search for feature subsets with different sizes. It can fix the selection states of some features and focus on the others when updating particles, thus narrowing the large search space.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Computer Science, Artificial Intelligence
Zi-Jia Wang, Jun-Rong Jian, Zhi-Hui Zhan, Yun Li, Sam Kwong, Jun Zhang
Summary: This article proposes a method called GT-based DE to solve large-scale optimization problems by targeting and modifying certain values in bottleneck dimensions. Experimental results show that GTDE is efficient and performs better or at least comparable to other state-of-the-art algorithms in solving LSOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Min Gao, Jian-Yu Li, Chun-Hua Chen, Yun Li, Jun Zhang, Zhi-Hui Zhan
Summary: In this study, an enhanced MKR (EMKR) approach is proposed to address the two difficult issues in knowledge graph-based recommender systems. The attention mechanism and relation-aware graph convolutional neural network are utilized to capture users' historical behavior patterns and deep multi-relation semantic information. Additionally, a two-part modeling strategy is introduced for better representation of users in datasets with different sparsity. Experimental results show that EMKR outperforms state-of-the-art approaches, especially in sparse user-item interactions.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Automation & Control Systems
Sheng-Hao Wu, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang
Summary: This article proposes a new evolutionary multitask optimization algorithm (EMTO) to address the similarity measurement and knowledge transfer issues. By considering the shift invariance between tasks, the proposed algorithm clusters similar tasks and transfers successful parameters among them. Experimental results demonstrate the superiority of the proposed algorithm in solving many-task optimization problems (MaTOPs).
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Xiao-Fang Liu, Yongchun Fang, Zhi-Hui Zhan, Jun Zhang
Summary: Cooperative heterogeneous multirobot systems have been gaining attention recently for executing complex tasks using multiple heterogeneous robots. Allocating these robots to cooperative tasks is a significant optimization problem, and existing methods are not sufficient to address it. This study proposes a multiobjective model and a strength learning particle swarm optimization (SLPSO) to optimize multiple objectives. Experimental results demonstrate that SLPSO outperforms existing algorithms in terms of inverted generational distance and hypervolume metrics.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Yi Jiang, Zhi-Hui Zhan, Kay Chen Tan, Jun Zhang
Summary: This article proposes a block-level knowledge transfer (BLKT) framework to overcome the limitations of knowledge transfer in multitask optimization problems. BLKT divides individuals into blocks and transfers knowledge at the block-level, enabling transfer between similar dimensions belonging to the same or different tasks. Extensive experiments show that BLKT-based differential evolution outperforms state-of-the-art algorithms in multitask optimization and also achieves competitive performance in single-task global optimization.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Review
Environmental Sciences
Zhihan Gao, Min Gao, Chun-Hua Chen, Yifan Zhou, Zhi-Hui Zhan, Yuan Ren
Summary: In this study, a self-developed text extraction tool called LitStraw was used to extract, analyze, and construct knowledge graphs from nearly 900 PDF research papers collected from Web of Science from 2000 to 2021. The results showed a growing number of interdisciplinary collaborations in wastewater-based epidemiology (WBE), with close collaboration between the USA, Australia, China, and European countries. While illicit drugs and pharmaceuticals remain hot research topics, specific research focuses have changed significantly, including new psychoactive substances, biomarkers, and stability. Additionally, the detection of SARS-CoV-2 RNA in sewage has become a major research focus since 2020. This work provides insights into the development of WBE and offers new ideas for paper mining analysis methods in different fields.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Ye-Qun Wang, Jian-Yu Li, Chun-Hua Chen, Jun Zhang, Zhi-Hui Zhan
Summary: This research proposes a particle swarm optimization approach called SAFE-PSO that tackles the optimization problem of neural networks. Experimental results show that SAFE-PSO is effective and efficient on widely used datasets.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
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)
