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M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing

PROCESSES 2023年第4期11卷 1053页

作者： Dai, Zhiming Ding, Weichao Min, Qi Gu, Chunhua Yao, Baohua Shen, Xiaohan East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China Shanghai Jian Qiao Univ Sch Informat Technol Shanghai 201306 Peoples R China Shanghai Inst Civil Def Sci Shanghai 200020 Peoples R China

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.

关键词： fog computing task scheduling multi-objective evolutionary algorithm MOEA D

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multi-objective evolving long-short term memory networks with attention for network intrusion detection

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APPLIED SOFT COMPUTING 2023年 139卷

作者： Wei, Wenhong Chen, Yi Lin, Qiuzhen Ji, Junkai Wong, Ka-Chun Li, Jianqiang Dongguan Univ Technol Sch Comp Sci & Technol Dongguan Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Cyber security has received increasing attention, as people use more Internet applications in their lives and worry about the security of their personal data on the Internet. Intrusion Detection Systems (IDSs) are critical security tools that can detect and respond to intrusions. In recent years, Deep Learning (DL) techniques have gained popularity in IDS design due to their promising performance in terms of detection accuracy. However, the design of DL architectures usually requires professional knowledge and significantly impacts the performance of the DL model. Furthermore, the existence of a small ratio of abnormal traffic in vast network traffic leads to a serious imbalanced data problem, which negatively affects the performance of the DL model in detecting minority attack classes. To alleviate these problems, this paper proposes a multi-objective evolutionary DL model (called EvoBMF) to detect network intrusion behaviors. The model incorporates bidirectional Long-short Term Memory (BiLSTM) for preliminary feature extraction, multi-Head Attention (MHA) for further capturing features and global information of the network traffic, and Full-Connected Layer (FCL) module to perform final classification. To deal with the challenge of manually tuning the parameters of the DL model when tackling different tasks, the parameters of the EvoBMF model are first encoded as the chromosome of the multi-objective evolutionary algorithm (MOEA), which aims to optimize the two conflicting objectives (complexity and classification ability) of the model. A state-of-the-art MOEA (MOEA/D-DRA) is then used to optimize the above two objectives, aiming to obtain the optimal architecture for EvoBMF, which can be easily deployed in cloud computing scenarios to detect and respond to network intrusions. Additionally, to alleviate the severe imbalance in routine network traffic, the synthetic minority over-sampling technique is introduced to generate representative samples of minority classes

关键词： Intrusion detection system Neural architecture search multi-objective evolutionary algorithm

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A multi-stage ensemble model for power load forecasting based on decomposition, error factors, and multi-objective optimization algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年第PartB期155卷

作者： Fan, Chaodong Nie, Shanghao Xiao, Leyi Yi, Lingzhi Wu, Yuetang Li, Gongrong Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Peoples R China Hunan Software Vocat & Tech Univ Sch Software & Informat Engn Xiangtan 411100 Peoples R China Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410205 Peoples R China

Short-term power load forecasting plays an important role in ensuring the stable operation of power systems and improving economic benefits. However, most of the previous studies ignored the limitations of a single pre-diction model and the useful information in the error factors, resulting in low prediction accuracy. Therefore, this paper proposes a multi-stage integrated model based on decomposition, error factors, and a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The proposed model consists of three stages: in the first stage, the gated recurrent unit (GRU) is used to predict the components of complete ensemble empirical modal decomposition with adaptive noise, and new data sets are obtained by combining them with the original data sets to fully mine the data characteristics. In the second stage, the MOEA/D based on angle and distance selection strategy and adaptive population generation strategy is used to optimize GRU network parameters with accuracy and diversity as the objective functions, obtaining several load forecasting models and error forecasting models that consider accuracy and diversity. In the third stage, a new nonlinear integration method based on GRU optimized by MOEA/D is used to integrate load forecasting values and error forecasting values, considering error factors to further improve forecasting accuracy. Experimental results on the Australian wholesale electricity market and energy market datasets show that the proposed model outperforms the comparative model in terms of accuracy and generalization and can be widely applied in load forecasting.

关键词： Short-term load forecasting GRU network Ensemble learning multi-objective evolutionary algorithm

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A decomposition-based multi-objective optimization approach for balancing the energy consumption of wireless sensor networks

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APPLIED SOFT COMPUTING 2021年 107卷 107365-107365页

作者： Nguyen Thi Tam Tran Huy Hung Huynh Thi Thanh Binh Le Trong Vinh Vietnam Natl Univ Univ Sci Hanoi Vietnam Hanoi Univ Sci & Technol Hanoi Vietnam

Wireless sensor networks consist of many sensor nodes with limited resources and computing capability. Thus, managing energy consumption to prolong network lifetime is a critical issue. Several approaches have been proposed to extend the network lifetime, one of which involves deploying relay nodes to transfer data from sensors to the base station. However, the limited number of relay nodes is a challenge that often goes overlooked. This paper examines the problem of optimizing the network lifetime and the number of relay nodes in three-dimensional terrains. A novel algorithm called MOEA/D-LS is proposed with the aim of obtaining a better tradeoff between two objectives. The algorithm is a hybridization between multiobjective evolutionary algorithm based on decomposition, and a special local search to optimize the former's subproblems. Simulation results on 3D datasets show that the proposed algorithm has a significantly better performance compared with existing algorithms on all measured metrics. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Wireless sensor networks Network lifetime multi-objective evolutionary algorithm

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multi-objective dynamic distribution adaptation with instance reweighting for transfer feature learning

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KNOWLEDGE-BASED SYSTEMS 2023年第1期263卷

作者： Li, Haoran He, Fazhi Pan, Yiteng Xidian Univ State Key Lab Integrated Serv Networks Xian Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

In knowledge adaptation, the source and target knowledge are transferred into the same mapping space by simultaneously reducing the difference between the marginal and conditional distributions;however, it is difficult to precisely balance the two distributions at each transformation. To address this problem, a novel multi-objective dynamic distribution adaptation (MODDA) with instance reweighting is proposed to reduce discrepancies between the two distributions. In addition, a customised non-dominated sorting genetic algorithm-II (NSGA2) optimisation method is presented for searching the optimal cumulative weight path, and four genetic operator combinations are compared to determine which one is optimal for MODDA. Moreover, kernel mean matching is proposed for the first time for dynamic compensation based on an individual's relevance in instance reweighting. The experimental results confirm that MODDA outperforms other state-of-the-art algorithms in terms of the classification accuracy for 16 well-known cross-domain tasks.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Domain adaptation multi-objective evolutionary algorithm Instance reweighting

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Evolving filter criteria for randomly initialized network pruning in image classification

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NEUROCOMPUTING 2024年 594卷

作者： Chen, Xiangru Liu, Chenjing Hu, Peng Lin, Jie Gong, Yunhong Chen, Yingke Peng, Dezhong Geng, Xue Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Agcy Sci Technol & Res Inst Infocomm Res Singapore 138632 Singapore Sichuan Newstrong UHD Video Technol Co Ltd Chengdu 610095 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST England

Network pruning aims to enhance the performance of deep neural networks by eliminating redundant components from the model. However, existing pruning methods typically require a well -trained model and employ fixed, single pruning criteria throughout the pruning cycles. To address these limitations, we propose a novel method called evolutionary Filter Criteria (EvoFC). This method enables the automated search for the network pruning ratio and criterion during a population -based heuristic search process. We introduce a unique encoding space that represents the chosen pruning criterion and ratio for each layer, facilitating the acquisition of optimal architecture configurations for candidate networks during iterations. Additionally, we devise a novel weight inheritance mechanism to mitigate the computational burden associated with the population -based nature of the method, resulting in a significant reduction in overall training time. We validate our method by applying it to randomly initialized networks and conducting empirical experiments on CIFAR-10/100, ILSVRC2012 and Places365 datasets. The results demonstrate that our method effectively reduces the number of FLOPs while striking a fine balance between accuracy and computational efficiency. This underscores the practical value of our method in optimizing performance while efficiently utilizing computational resources, particularly when pruning networks starting from random initialization.

关键词： Genetic algorithm Evolving deep learning Network pruning multi-objective evolutionary algorithm

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multi-objective Optimization Design of Plate-type Fuel via Co-simulation Method

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ANNALS OF NUCLEAR ENERGY 2022年 169卷 108914-108914页

作者： Tan, Jiatong Li, Quan Zhao, Bo Ma, Chao Wu, Yingwei Li, Yuanming Zhang, Jing He, Yanan Ye, Tianzhou Zhang, Cheng Tian, Wenxi Su, Guanghui Qiu, Suizheng Xi An Jiao Tong Univ Sch Nucl Sci & Technol State Key Lab Multiphase Flow Power Engn Shaanxi Key Lab Adv Nucl Energy & Technol Xian 710049 Peoples R China Nucl Power Inst China 328 Huayang Changshun Ave Sect 1 Chengdu Peoples R China

Plate-type fuel element has been implemented and studied widely because of its good heat transfer capa-bility. For the actual design of fuel assembly, the design objectives usually to be in competition, and the constraints also need to be considered. Therefore, this is a challenging constrained multi-objective opti-mization problem. A co-simulation method has been developed in this study to optimize the design of fuel plate for the thermal-hydraulic performance. Moreover, due to the versatility of this co -simulation method, extra objectives and constraints could be added as needed. First, a two-dimensional thermal-fluid-solid multiphysics model of plate-type fuel assembly was established and solved by the finite element method. After that, the values of relevant physical variables were verified with the three-dimensional model. Finally, the numerical simulation of multiphysics model was incorpo-rated with the multi-objective evolutionary algorithm (NSGA-II, adaptive NSGA-III and CMOEA-MS) which could handle constraints to optimize the thickness parameters of fuel plate. Pareto optimal solu-tions were obtained separately after the co-simulation with three algorithms, and the qualitative and quantitative analyses of the Pareto fronts showed that CMOEA-MS and NSGA-II had better performance in this study. This co-simulation could provide comprehensive and reliable design information according to the design objectives and requirements. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Plate -type fuel multi-objective optimization multi-objective evolutionary algorithm multiphysics

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An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand

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KNOWLEDGE-BASED SYSTEMS 2021年 230卷 107378-107378页

作者： Niu, Yunyun Kong, Detian Wen, Rong Cao, Zhiguang Xiao, Jianhua China Univ Geosci Beijing Sch Informat Engn Beijing 100083 Peoples R China Singapore Inst Mfg Technol Singapore 138634 Singapore Nankai Univ Res Ctr Logist Tianjin 300071 Peoples R China

The multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is much harder to tackle than other traditional vehicle routing problems (VRPs), due to the uncertainty in customer demands and potentially conflicted objectives. In this paper, we present an improved multi-objective learnable evolution model (IMOLEM) to solve MO-VRPSD with three objectives of travel distance, driver remuneration and number of vehicles. In our method, a machine learning algorithm, i.e., decision tree, is exploited to help find and guide the desirable direction of evolution process. To cope with the key issue of "route failure" caused due to stochastic customer demands, we propose a novel chromosome representation based on priority with bubbles. Moreover, an efficient nondominated sort using a sequential search strategy (ENS-SS) in conjunction with some heuristic operations are leveraged to handle the multi-objective property of the problem. Our algorithm is evaluated on the instances of modified Solomon VRP benchmark. Experimental results show that the proposed IMOLEM is capable to find better Pareto front of solutions and also deliver superior performance to other evolutionary algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Vehicle routing problems Stochastic demand Learnable evolution model multi-objective evolutionary algorithm

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Fault diagnosis of planetary gearbox using multi-criteria feature selection and heterogeneous ensemble learning classification

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MEASUREMENT 2021年 173卷 108654-108654页

作者： Wang, Zirui Huang, Haian Wang, Youren Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Jiangsu Peoples R China

Fault feature selection and early fault diagnosis for planetary gearbox are important tasks and have been widely investigate. This paper proposes a novel fault diagnosis scheme for planetary gearbox using multi-criteria fault feature selection and heterogeneous ensemble learning classification. Vibration signals collected by the acceleration sensors are imported for fault diagnosis of planetary gearbox. High dimension fault features are extracted by analyzing the vibration signals in time domain, frequency domain and time-frequency domain. The criteria for selecting lower dimension optimal fault features of planetary gearbox are designed, and the mathematic model for fault feature selection with multi-criteria is established. After that, a new feature selection method using multi-objective evolutionary algorithm based on Decomposition (MOEA/D) is applied to obtain diverse lower dimension quasi optimal fault feature subsets. Then each quasi optimal fault feature subset is transferred to a base classifier for primary fault diagnosis. Those base classifications are performed by support vector machine and sparse Bayesian extreme learning machine respectively. Dezert-Smarandache rules are applied for classifier-level fusion to achieve and evaluate overall accuracy of the fault diagnosis for planetary gearbox. Experimental results state that the proposed method constantly gets diverse lower dimension quasi optimal fault features smoothly, and significantly improves the accuracy and robustness of fault diagnosis.

关键词： Planetary gearbox fault diagnosis vibration signal multi-criteria feature selection multi-objective evolutionary algorithm Dezert-Smarandache theory heterogeneous ensemble learning

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multi-objective robust mathematical modeling of emergency relief in disaster under uncertainty

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SCIENTIA IRANICA 2022年第5期29卷 2670-2695页

作者： Eshghi, A. A. Tavakkoli-Moghaddam, R. Ebrahimnejad, S. Ghezavati, V. R. Islamic Azad Univ Sch Ind Engn South Tehran Branch Tehran Iran Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Islamic Azad Univ Dept Ind Engn Karaj Branch Karaj Iran

This paper presents a robust location-allocation planning problem for emergency relief in a disaster situation. It is a multi-objective, multi-commodity, multi-vehicle, and multi-level logistics model that considers injury variety through service prioritizing for injuries. Furthermore, it incorporates unmet demand for particular item types in various damaged areas. Public donation of different relief goods through capacitated medical centers and emergency centers is also addressed with regard to damage type, capacitated relief distribution centers, and disaster management centers. The model is a non-linear mixed-integer programming that simultaneously optimizes three objectives, namely maximizing service fairness to damaged areas, maximizing fair commodity disaster management, and minimizing the total logistics cost. To solve such a hard problem, a Non-dominated Sorting Genetic algorithm-II (NSGA-II) was developed and the Taguchi method was employed to adjust its parameters. The "-constraint method was used for the evaluation of the performance of the proposed algorithm. For more accurate validation, three comparison metrics including diversification, spacing, and mean ideal distance were adopted. The results verified the effectiveness of the algorithm in a reasonable computational time. Eventually, to examine the applicability of the presented model and the proposed algorithm, a case study was analyzed in an area located in the north of Iran, known for historical earthquake records and aggregated active faults. (c) 2022 Sharif University of Technology. All rights reserved.

关键词： Location-allocation planning Robust optimization Emergency relief Disaster multi-objective evolutionary algorithm

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