Article
Computer Science, Hardware & Architecture
Yang Wang, Qian Hu, Long Nguyen, Maryam Jalalitabar
Summary: Network virtualization can alleviate the ossification of the current Internet by abstracting logical services as virtual networks mapped to physical infrastructure. This paper addresses the issue of decomposing the Node Assignment (NA) and Link Mapping (LM) sub-problems in the virtual network embedding (VNE) process, and proposes an iterative decomposition approach that achieves a balance between time and optimality. Evaluation results demonstrate the superiority of the proposed schemes over benchmark solutions.
Article
Mathematics
Omer Ali, Qamar Abbas, Khalid Mahmood, Ernesto Bautista Thompson, Jon Arambarri, Imran Ashraf
Summary: This study introduces a competitive coevolution process to enhance the capability of Phasor PSO (PPSO) for global optimization problems. Experimental results show that the improved competitive multi-swarm PPSO (ICPPSO) algorithm achieves a dominating performance, with average improvements of 15%, 20%, 30%, and 35% over PPSO and FMPSO.
Article
Computer Science, Artificial Intelligence
Md Anisul Islam, Yuvraj Gajpal, Tarek Y. ElMekkawy
Summary: This paper studies the Clustered Vehicle Routing Problem (CluVRP), introduces a new hybrid metaheuristic algorithm to solve the problem, and demonstrates the algorithm's superiority in performance on a large number of benchmark instances.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Chemical
Szu-Chou Chen, Wen-Chen Huang, Ming-Hsien Hsueh, Chieh-Yu Pan, Chih-Hao Chang
Summary: The antlion optimization algorithm (ALO) has some drawbacks, such as long runtime, which hinders decision makers. To address the slow convergence rates, a novel exponential-weighted antlion optimization algorithm (EALO) is proposed, which has demonstrated higher convergence rate and better experimental results in comparison to existing methods.
Article
Automation & Control Systems
Guido Marchetto, Riccardo Sisto, Fulvio Valenza, Jalolliddin Yusupov, Adlen Ksentini
Summary: The article proposes a framework for reliable placement of services across physically separated locations, which offers system optimization and connectivity policy enforcement. This is achieved by exploiting optimization modulo theories to solve the virtual network embedding problem.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Yilu Liu, Jing Liu, Xiangyi Teng
Summary: This paper proposes a single-particle optimization algorithm (SPO-NE) for network embedding problems, which aims to preserve both the local properties of nodes and the global community structure of networks. Experimental results show that SPO-NE outperforms several state-of-the-art baselines on various network tasks.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Xiaosong Yu, Lu Lu, Yongli Zhao, Feng Wang, Avishek Nag, Xinghua Li, Jie Zhang
Summary: With the rise of cloud services, demands for bandwidth-intensive applications have increased, leading to a more diversified development of application services. Optical network virtualization and flexible-grid elastic optical networks are considered key technologies for the future Internet infrastructure, with the co-existence of fixed/flexible grid optical networks presenting new challenges and solutions for virtual optical network provisioning.
Article
Computer Science, Information Systems
Fengrong Han, Izzeldin Ibrahim Mohamed Abdelaziz, Kamarul Hawari Ghazali, Yue Zhao
Summary: This study firstly comprehensively reviews and classifies frequently-used localization fitness functions in the range-free localization scheme. Then, multiple experiments are conducted for each typical localization fitness function, and the experimental results are analyzed in terms of accuracy and stability. Additionally, the advantages and disadvantages of each localization fitness function are discussed. Finally, an advanced localization fitness function is proposed based on the experimental results, providing guidance and reference for the selection and improvement of fitness functions in the range-free localization algorithm.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Seema A. Alsaidy, Amenah D. Abbood, Mouayad A. Sahib
Summary: Task scheduling is a significant issue in cloud computing, and this paper proposes an improved initialization method for particle swarm optimization (PSO) using heuristic algorithms. By initializing PSO with longest job to fastest processor (LJFP) and minimum completion time (MCT) algorithms, the performance can be significantly enhanced, compared to traditional PSO and other methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Mohammad Zain Ul Abideen, Omar Ellabban, Furkan Ahmad, Luluwah Al-Fagih
Summary: This study proposes two algorithms to consider the adverse effects of DERs integration on the distribution network when calculating HC, tested on the IEEE 123 bus network. The modified iterative method performs well for large-scale DER cases, while the RPSO method is suitable for multiple DER situations. Therefore, careful consideration is needed when selecting HC calculation methods based on specific applications.
Article
Automation & Control Systems
Bowen Zhao, Ximeng Liu, An Song, Wei-Neng Chen, Kuei-Kuei Lai, Jun Zhang, Robert H. Deng
Summary: This article proposes a privacy-preserving multiagent PSO algorithm (PriMPSO) that protects each particle's data and enables private data sharing in a distributed computing paradigm. It designs a privacy-preserving exemplar selection algorithm and a privacy-preserving triple computation protocol to select exemplars and update particles, respectively. Privacy analyses and experiments confirm that PriMPSO protects particles' privacy and has uniform convergence performance with the existing PSO algorithm in approximating an optimal solution.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Lalit Kumar, Manish Pandey, Mitul Kumar Ahirwal
Summary: The computational time of swarm optimization algorithms, including Particle Swarm Optimization (PSO), is increased due to the large number of decision variables in complex problems. A new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm, combining PSO and Jaya algorithm, is proposed to provide a more parallel version of the algorithm. The proposed algorithm outperforms other parallel PSO versions and Jaya algorithm in terms of computational time and optimal solution.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Chinmaya Kumar Dehury, Prasan Kumar Sahoo
Summary: This article introduces a failure-aware semi-centralized VNE algorithm to reduce the impact of resource failures on cloud computing users, and demonstrates the superiority of this algorithm through simulation experiments.
IEEE TRANSACTIONS ON CLOUD COMPUTING
(2022)
Article
Social Sciences, Interdisciplinary
Iqbal Hayat, Adnan Tariq, Waseem Shahzad, Manzar Masud, Shahzad Ahmed, Muhammad Umair Ali, Amad Zafar
Summary: Permutation flow-shop scheduling is a strategy that optimizes the processing of jobs while ensuring the same order on subsequent machines. Particle Swarm Optimization (PSO) has been frequently used for this purpose. This research developed a standard PSO and hybridized it with Variable Neighborhood Search (PSO-VNS) and Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The hybrid PSO (HPSO) performed well compared to other algorithms, with an ARPD value of 0.48 indicating robustness and improved performance in optimizing makespan.
Article
Computer Science, Artificial Intelligence
Yuan Gao
Summary: This study predicts museum tourism demand using neural network integration algorithms, finding that the QPSO-BPNN algorithm has a higher prediction accuracy and is less sensitive to the size of the population.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jinkun You, Yuan-Gen Wang, Guopu Zhu, Ligang Wu, Hongli Zhang, Sam Kwong
Summary: This paper proposes an equivalent keys (EK)-based estimator for estimating the secret key in both traditional and more secure spread spectrum (SS) watermarking methods. The proposed estimator selects equivalent keys by adding up uniformly sampled equivalent keys from the equivalent region. The experimental results validate the theoretical analysis and demonstrate the superiority of the proposed estimator over existing methods. Moreover, this paper reveals the insecurity of the more secure SS watermarking methods in the known-message attack (KMA) scenario for the first time.
IEEE TRANSACTIONS ON MULTIMEDIA
(2023)
Article
Computer Science, Information Systems
Dinghao Yang, Wei Gao, Ge Li, Hui Yuan, Junhui Hou, Sam Kwong
Summary: In this paper, an efficient point cloud classification method based on manifold learning is proposed. The method embeds point cloud features using manifold learning algorithms to consider the geometric continuity on the surface. Experimental results show that the proposed method outperforms existing methods and achieves better classification accuracy.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2023)
Article
Computer Science, Theory & Methods
Yun Zhang, Linwei Zhu, Gangyi Jiang, Sam Kwong, C-C Jay Kuo
Summary: In order to provide users with more realistic visual experiences, videos are developing in the trends of UHD, HFR, HDR, WCG, and high clarity. However, the increasing data amount of videos requires efficient video compression for storage and network transmission. Perceptually optimized video coding aims to maximize compression efficiency by exploiting visual redundancies. This article presents a comprehensive survey on perceptually optimized video coding, including problem formulation, recent advances, optimizations, and challenging issues.
ACM COMPUTING SURVEYS
(2023)
Article
Engineering, Multidisciplinary
Benxin Zhang, Guopu Zhu, Zhibin Zhu, Sam Kwong
Summary: This paper proposes a nonconvex log total variation model for image restoration, and presents a specific alternating direction method of multipliers to solve the model. Experimental results demonstrate that the proposed method is effective in image denoising, deblurring, computed tomography, magnetic resonance imaging, and image super-resolution.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Computer Science, Artificial Intelligence
Feng-Feng Wei, Wei-Neng Chen, Qing Li, Sang-Woon Jeon, Jun Zhang
Summary: This article defines distributed expensive constrained optimization problems (DECOPs) and proposes a distributed evolutionary constrained optimization algorithm with on-demand evaluation (DEAOE). DEAOE adaptively evolves different constraints in an asynchronous way through on-demand evaluation, improving population convergence and diversity. Experimental results demonstrate that DEAOE outperforms centralized state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) in terms of performance and efficiency.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Civil
Wanyi Zhou, Xiaolin Xiao, Yue-Jiao Gong, Jia Chen, Jun Fang, Naiqiang Tan, Nan Ma, Qun Li, Chai Hua, Sang-Woon Jeon, Jun Zhang
Summary: This study proposes a method of formulating the traffic network as a temporal attributed graph and performing node representation learning on it to address the problem of travel time estimation. The learned representation can jointly exploit dynamic traffic conditions and the topology of the road network, and estimate travel time using a route-based spatio-temporal dependence learning module.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Yue-Jiao Gong, Ting Huang, Yi-Ning Ma, Sang-Woon Jeon, Jun Zhang
Summary: This paper focuses on the multiple-trajectory planning problem for automatic underwater vehicles (AUVs) and proposes a comprehensive model that considers the complexity of underwater environments, efficiency of each trajectory, and diversity among different trajectories. To solve this problem, an ant colony-based trajectory optimizer is developed, incorporating a niching strategy, decayed alarm pheromone measure, and diversified heuristic measure for improved search effectiveness and efficiency. Experimental results demonstrate that the proposed algorithm not only provides multiple AUV trajectories for flexible choice, but also outperforms state-of-the-art algorithms in terms of single trajectory efficiency.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Junna Zhang, Degang Chen, Qiang Yang, Yiqiao Wang, Dong Liu, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a novel differential evolution framework called proximity ranking-based multimodal differential evolution (PRMDE) for multimodal optimization. Through the cooperative cooperation among three main mechanisms, PRMDE is capable of locating multiple global optima simultaneously. Experimental results show that PRMDE is effective and achieves competitive or even better optimization performance than several representative methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Dong Liu, Hao He, Qiang Yang, Yiqiao Wang, Sang-Woon Jeon, Jun Zhang
Summary: This paper proposes a simple and effective mutation scheme named DE/current-to-rwrand/1 to enhance the optimization ability of differential evolution (DE) in solving complex optimization problems. The proposed mutation strategy, called function value ranking aware differential evolution (FVRADE), balances high diversity and fast convergence of the population. Experimental results demonstrate that FVRADE outperforms several state-of-the-art methods and shows promise in solving real-world optimization problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Information Systems
En Zhang, Zihao Nie, Qiang Yang, Yiqiao Wang, Dong Liu, Sang-Woon Jeon, Jun Zhang
Summary: Facing complex large-scale optimization problems, most existing optimization algorithms lose their effectiveness. In order to effectively solve this type of problem, we propose a heterogeneous cognitive learning particle swarm optimization algorithm (HCLPSO). Unlike most particle swarm optimization algorithms, HCLPSO partitions particles into superior and inferior categories based on their fitness and treats them differently. With the collaboration of two learning mechanisms, HCLPSO can effectively explore the search space and exploit the found optimal zones to find optimal solutions.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Artificial Intelligence
Rongqun Lin, Meng Wang, Pingping Zhang, Shiqi Wang, Sam Kwong
Summary: Recently, there has been significant research attention on learned video compression. However, existing methods use a single hypothesis for motion alignment, leading to inaccurate motion estimation, especially for complex scenes. Inspired by the multiple hypotheses philosophy, we propose a multiple hypotheses based motion compensation approach to enhance efficiency by providing diverse hypotheses. We also introduce a hypotheses attention module and utilize context combination to fuse weighted hypotheses and generate effective contexts for compression.
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
Feng-Feng Wei, Wei-Neng Chen, Wentao Mao, Xiao-Min Hu, Jun Zhang
Summary: This article proposes an efficient two-stage surrogate-assisted differential evolution (eToSA-DE) algorithm to handle expensive inequality constraints. The algorithm trains a surrogate model for the degree of constraint violation, with the type of surrogate changing during the evolution process. Both types of surrogates are constructed using individuals selected by the boundary training data selection strategy. A feasible exploration strategy is devised to search for promising areas. Extensive experiments demonstrate that the proposed method can achieve satisfactory optimization results and significantly improve the efficiency of the algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Xiao-Fang Liu, Jun Zhang, Jun Wang
Summary: This article presents a cooperative differential evolution algorithm with an attention-based prediction strategy for dynamic multiobjective optimization. Multiple populations are used to optimize multiple objectives and find subparts of the Pareto front. The algorithm achieves a balanced approximation of the Pareto front and adapts to changes in the environment by using a new attention-based prediction strategy. Experimental results demonstrate the superiority of the proposed method to state-of-the-art algorithms.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Jinpeng Chen, Runmin Cong, Horace Ho Shing Ip, Sam Kwong
Summary: This article introduces a keypoints-based salient instance segmentation network, which utilizes keypoints as geometric guidance for dynamic convolutions and achieves precise segmentation through differentiated patterns fusion module and high-level semantic guided saliency module.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
