Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization Problems

Shao, Yinan; Lin, Jerry Chun‐Wei; Srivastava, Gautam; Guo, Dongdong; Zhang, Hongchun; Hu, Yi; Jolfaei, Alireza

doi:10.1109/tnnls.2021.3105937

Cited by 53 publications

(16 citation statements)

References 27 publications

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“…The main aim of this model is to reconstruct the missing information that was lost due to quantization, gamma correction, or tone mapping. Shao et al 42 utilized the multi‐objective neural evolutionary algorithm (MONEADD) for solving the combinatorial optimization problem. The efficiency of the MONEADD technique is verified in terms of the travelling salesman problem and knapsack problem which consists of a total of 200 instances.…”

Section: Review Of Related Workmentioning

confidence: 99%

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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Cloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

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Section: Review Of Related Workmentioning

confidence: 99%

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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show abstract

“…The problem formulated in Sect. 2 is a bi-objective combinatorial optimization problem [4][5][6][7][8][9][10], which is generally difficult to solve when the number of the decision variable becomes large. Instead of using complex algorithms to solve the bi-objective combinatorial optimization problem, an overall fitness function is proposed and a solver based on evolutionary computation is used to efficiently obtain good solutions.…”

Section: The Proposed Approachmentioning

confidence: 99%

Final Report Assignment Based on Students’ Preferences to Topics and Partners

Hsu¹,

Chen²,

Kuo³

et al. 2022

Proceedings of the 2022 International Conference on Diversified Education and Social Development (DESD 2022)

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To evaluate the learning performance of students, a final report is usually assigned. Students are asked to form a group which includes several students and they are assigned a specific topic to prepare a report. The way of assignment greatly influences on the results. The purpose of this study is to present a novel optimization approach to final report assignment which considers the preferences of students to topics as well as their partners. Initially, students are asked to indicate their preferences to some specific topics and their preferred co-operative partners. After aggregating preference data, a scoring system is used to evaluate the assignment. Mathematical Programming (MP) is employed and an evolutionary method is used to find solutions. Results from this study show that the proposed approach presented in this study can get good solutions efficiently.

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“…Recently, artificial neural networks have been widely used and achieved outstanding performances in fields such as computer vision [12], edge computing [13,14,15,16,17,18], anomaly detection [19,20,21], data mining [22,23,24,25], algorithm optimization [26,27,28,29], medical diagnosis [30,31], and climate prediction [32], etc. This has attracted widespread attention from researchers in the field of weather forecasting, and they began to apply these networks to radar echo extrapolation.…”

Section: Introductionmentioning

confidence: 99%

A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars

Yang¹,

Wu²,

Liu³

et al. 2023

ISA Transactions

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Multi-Objective Neural Evolutionary Algorithm for Combinatorial Optimization Problems

Cited by 53 publications

References 27 publications

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Final Report Assignment Based on Students’ Preferences to Topics and Partners

A self-attention integrated spatiotemporal LSTM approach to edge-radar echo extrapolation in the Internet of Radars

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