Article
Chemistry, Multidisciplinary
Dan-Dan Yang, Meng Mei, Yu-Jun Zhu, Xin He, Yong Xu, Wei Wu
Summary: The enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) proposed in this paper optimizes network coverage, node utilization, and network energy balance objectives effectively, improving coverage optimization in wireless sensor networks in complex environments.
APPLIED SCIENCES-BASEL
(2023)
Article
Automation & Control Systems
Nguyen Thi Tam, Vu Dinh Hoang, Huynh Thi Thanh Binh, Le Trong Vinh
Summary: Coverage is crucial for the performance and proper functioning of wireless sensor networks, but it faces challenges due to limited sensing range, communication range, and energy of the sensors. This paper proposes an approach based on the teaching-learning based optimization algorithm to solve the network coverage problem and compares it with other methods through experimental results, showing significant improvements in different metrics.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Zan Yang, Haobo Qiu, Liang Gao, Liming Chen, Jiansheng Liu
Summary: This paper proposes an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) for efficiently solving expensive constrained multi-objective optimization problems. With three specific search strategies, ASA-MOEA/D achieved targeted searches for different subproblems based on their optimization states. The framework maintained feasibility, convergence, and diversity through the use of RBF surrogates and exploration of unexplored subregions. Empirical studies showed that ASA-MOEA/D with tchebycheff approach outperformed four state-of-the-art algorithms.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Jie Cao, Jianlin Zhang, Fuqing Zhao, Zuohan Chen
Summary: A novel algorithm named MOEA/D-TS is proposed in this paper, which effectively solves multi-objective optimization problems through two-stage evolution strategies. The performance of the algorithm is validated in real world problems and shows advantages in terms of convergence and diversity over other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Ying Xu, Huan Zhang, Lei Huang, Rong Qu, Yusuke Nojima
Summary: This research investigates the grid-based decomposition methods in multi-objective optimization to address the issues of diversity and convergence. A new concept of Pareto Front grid and a statistical analysis-based nadir point estimation strategy are proposed to improve computational efficiency. Furthermore, a novel grid-based knee point selection method is proposed. Experimental analysis demonstrates the effectiveness of the proposed PFG-MOEA algorithm.
APPLIED SOFT COMPUTING
(2023)
Article
Engineering, Civil
Lei Zhang, Tingting Liu, Xuefeng Ding
Summary: In this paper, a novel resource scheduling algorithm for WSNs is proposed based on differential ion coevolution and multi-objective decomposition. By introducing mobile nodes as relay nodes and building a multi-index evaluation model, the performance of WSNs is effectively optimized using a multi-objective decomposition strategy and differential ion coevolution strategy.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Abel Garcia-Najera, Saul Zapotecas-Martinez, Karen Miranda
Summary: The paper discusses the multi-objective optimization problem of cluster head selection in wireless sensor networks, using three multi-objective evolutionary algorithms, analyzing the conflicts between objectives, comparing the performance of the algorithms, and investigating the efficiency of the solutions in terms of network energy consumption.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
Saykat Dutta, Rammohan Mallipeddi, Kedar Nath Das
Summary: In this work, a Hybrid Selection based MOEA (HS-MOEA) is proposed, which effectively balances the diversity and convergence properties of MOEA by combining Pareto-dominance, reference vectors, and an indicator. Experimental simulations on DTLZ and WFG test suites demonstrate the superior performance of HS-MOEA compared to state-of-the-art MOEAs, with up to 10 objectives.
SCIENTIFIC REPORTS
(2022)
Article
Engineering, Electrical & Electronic
Syed Muhammad Hashir, Arefe Mehrabi, Mohammad Robat Mili, Mohamamd Javad Emadi, Derrick Wing Kwan Ng, Ioannis Krikidis
Summary: This paper investigates a UAV-enabled wireless powered communication network, where a UAV serves multiple energy constrained wireless sensor nodes, transmits wireless power to WSNs and designs resource allocation to maximize achievable sum rate and minimize downlink transmit power simultaneously. The formulated multi-objective optimization problem is addressed using a weighted Tchebycheff method, showing a trade-off between minimum power in the downlink and maximum rate as well as energy efficiency in the uplink.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Wei Zheng, Jianyong Sun
Summary: In this paper, a two-stage hybrid learning-based multi-objective evolutionary algorithm is proposed to address the balancing problem between convergence and diversity in multi-objective optimization problems. Experimental results show that the proposed algorithm outperforms nine state-of-the-art algorithms.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Shirin Tahmasebi, Nayereh Rasouli, Amir Hosein Kashefi, Elmira Rezabeyk, Hamid Reza Faragardi
Summary: This paper discusses the placement problem of SDN controllers in Wireless Sensor Networks, aiming to balance performance and synchronization cost through the Cuckoo optimization algorithm. Experimental results show that the proposed method significantly outperforms existing methods in terms of performance and synchronization cost, and is more scalable than Integer Linear Programming.
Article
Computer Science, Information Systems
V. Pandiyaraju, Sannasi Ganapathy, N. Mohith, A. Kannan
Summary: Wireless Sensor Networks (WSNs) are crucial in Precision Agriculture for real-time data collection. Efficient energy utilization and Cluster Head (CH) selection processes are addressed using a multi-objective clustering approach and a hybrid optimization technique. The proposed method also improves clustering algorithm precision and training accuracy by combining optimization techniques with convolutional neural networks.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Salmah Fattah, Ismail Ahmedy, Mohd Yamani Idna Idris, Abdullah Gani
Summary: Underwater wireless sensor networks have made significant contributions to ocean surveillance and monitoring. This study proposes a solution that combines an adaptive multi-parent crossover genetic algorithm with fuzzy dominance-based decomposition to address the deployment challenges of mobile underwater sensor nodes. The proposed solution outperforms other algorithms in terms of coverage rate, energy consumption, and system metrics, while ensuring maximum global convergence and lower computational complexity.
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
(2022)
Article
Computer Science, Artificial Intelligence
Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Baeck
Summary: The newly proposed AP-DI-MOEA algorithm can automatically generate preference regions and achieve better solutions within them, especially compared to other MOEA algorithms under the same budget.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Information Systems
Ruwang Jiao, Sanyou Zeng, Changhe Li, Yew-Soon Ong
Summary: This paper proposes two types of weight adjustments based on MOEA/D for solving highly constrained many-objective optimization problems, aiming to fully utilize promising feasible and infeasible solutions. Experimental results show that the proposed algorithm outperforms other algorithms in most cases, especially in highly constrained optimization problems.
INFORMATION SCIENCES
(2021)
Article
Environmental Sciences
Yuwei Guo, Zhuangzhuang Sun, Rong Qu, Licheng Jiao, Fang Liu, Xiangrong Zhang
Article
Management
Nelishia Pillay, Rong Qu
Summary: This paper focuses on the assessment of the generality performance of hyper-heuristics, introducing a new taxonomy and performance measure based on generality rather than optimality. Case studies are used to demonstrate the application of the generality performance measure, highlighting the importance of evaluating different types of hyper-heuristics based on their levels of generality.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Mathematics
Qiyi He, Xiaolin Meng, Rong Qu, Ruijie Xi
Article
Computer Science, Information Systems
Fuhong Song, Huanlai Xing, Shouxi Luo, Dawei Zhan, Penglin Dai, Rong Qu
IEEE INTERNET OF THINGS JOURNAL
(2020)
Article
Computer Science, Artificial Intelligence
Xingxing Hao, Rong Qu, Jing Liu
Summary: Hyper-heuristics and evolutionary multitasking share similarities in search methods, and by combining the advantages of both, the optimization of problems can be accelerated, leading to increased generality.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Weiyao Meng, Rong Qu
Summary: This paper introduces the AutoGCOP framework for the automated design of local search algorithms, optimizing the composition of algorithmic components and utilizing learning models for enhancement. The Markov chain model demonstrates superior performance in learning algorithmic component compositions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Geochemistry & Geophysics
Yuwei Guo, Licheng Jiao, Rong Qu, Zhuangzhuang Sun, Shuang Wang, Shuo Wang, Fang Liu
Summary: This paper proposes an adaptive fuzzy superpixel (AFS) algorithm based on polarimetric scattering information for PolSAR image classification. AFS utilizes the correlation between pixels' polarimetric scattering information to generate superpixels, and dynamically updates the ratio of undetermined pixels. Experimental results demonstrate the superiority of AFS in PolSAR image classification problems.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Editorial Material
Computer Science, Artificial Intelligence
Marco S. Nobile, Luca Manzoni, Daniel A. Ashlock, Rong Qu
Summary: Computational Intelligence (CI) provides powerful tools for complex computational tasks, including global optimization methods, machine learning, and fuzzy reasoning. In addition to algorithm improvement, CI research also focuses on representations and models to simplify optimization problems and reduce computational effort.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
(2023)
Article
Computer Science, Artificial Intelligence
Wenjie Yi, Rong Qu, Licheng Jiao
Summary: Automated algorithm design has become a popular research focus in solving complex combinatorial optimization problems. This study applies reinforcement learning to the automated design of metaheuristic algorithms within a general algorithm design framework. Two groups of features, search-dependent and instance-dependent, are identified to support effective reinforcement learning. Experimental results on a benchmark dataset demonstrate the effectiveness of the identified features in assisting automated algorithm design with the proposed reinforcement learning model.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jingyi Ding, Tiwen Wang, Ruohui Cheng, Licheng Jiao, Jianshe Wu, Jing Bai
Summary: In this paper, a new community evolution model is developed based on the universality of the timeframe, and a new optimized timeframe partitioning algorithm is proposed. The proposed self-adaptive timeframe partitioning algorithm improves the quality of community tracking and ensures the accuracy of prediction events in real-world networks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Xiaotong Li, Licheng Jiao, Hao Zhu, Fang Liu, Shuyuan Yang, Xiangrong Zhang, Shuang Wang, Rong Qu
Summary: This article proposes a collaborative learning tracking network for remote sensing videos, which includes CRFPF module, DSCA module, and GCRT strategy. Experimental results demonstrate the accuracy and effectiveness of this method in complex remote sensing scenes.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Uwe Aickelin, Hadi Akbarzadeh Khorshidi, Rong Qu, Hadi Charkhgard
Summary: This special issue focuses on the application of multiobjective evolutionary optimization in machine learning. Optimization plays a crucial role in many machine-learning techniques, and there is still potential to further utilize optimization in machine learning. Each machine-learning technique has hyperparameters that can be adjusted through evolutionary computation and optimization, considering multiple criteria such as bias, variance, complexity, and fairness in model selection. Multiobjective evolutionary optimization can help meet these criteria for optimizing machine-learning models. Although some existing approaches transform the problem into a single-objective optimization problem, multiobjective optimization models are more effective in contributing to multiple intended objectives or criteria.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Wenjie Yi, Rong Qu, Licheng Jiao, Ben Niu
Summary: This article proposes a general search framework (GSF) to unify different metaheuristic algorithms. With the established GSF, two reinforcement learning (RL)-based methods are developed to automatically design a new general population-based algorithm. The effectiveness and generalization of the proposed RL-based methods are comprehensively validated.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Zhiwen Xiao, Huanlai Xing, Bowen Zhao, Rong Qu, Shouxi Luo, Penglin Dai, Ke Li, Zonghai Zhu
Summary: Recently, contrastive learning has emerged as a promising method for learning discriminative representations from time series data. However, existing algorithms mostly focus on high-level semantic information, neglecting the importance of low-level semantic information. In this paper, we propose a novel deep contrastive representation learning with self-distillation (DCRLS) method, which combines data augmentation, deep contrastive learning, and self distillation. Experimental results show that the DCRLS-based structures achieve excellent performance on classification and clustering tasks.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
Article
Engineering, Electrical & Electronic
Huanlai Xing, Zhiwen Xiao, Rong Qu, Zonghai Zhu, Bowen Zhao
Summary: This article proposes an efficient federated distillation learning system (EFDLS) for multitask time series classification (TSC). It introduces two novel components: a feature-based student-teacher (FBST) framework and a distance-based weights matching (DBWM) scheme. Experimental results demonstrate that EFDLS outperforms other federated learning algorithms in multiple datasets and achieves higher mean accuracy compared to a single-task baseline.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
