A customized genetic algorithm for bi-objective routing in a dynamic network

Maskooki, Alaleh; Deb, Kalyanmoy; Kallio, Markku

doi:10.1016/j.ejor.2021.05.018

Cited by 18 publications

(7 citation statements)

References 41 publications

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“…The specific monitoring data flow is described in Figure 2 : Assuming that the intelligent equipment monitoring result is A and any intelligent equipment device A l = { a 1 , a 2 ,…, a n }, the relationship between A and each input monitoring data as follows:

where i,j,k are natural numbers, TS (·) is the dynamic programming function, f (·) is the stable wavelet function, G (·) is the forward function between different input indexes, and - G (·) is the reverse function to complete the dynamic monitoring. Suppose that the monitoring result a l of any sports event in intelligent health monitoring and the input z in the planning algorithm (data structure x i in intelligent equipment devices [ 11 ], impact of intelligent technology on health monitoring x j , and dynamic x k of health monitoring), p is the proportion of monitoring data (structured monitoring data >70%, semistructured monitoring data >70%, unstructured monitoring data >70%), q is the processing method of monitoring data distortion (reconstruction = 1, coefficient order = 2, quantification = 3, eigenvalue = 4, clustering = 4), then c l is described as Inc l o , p , q , o ∈ (1,2,….., n ), p ∈ (1,2,3,4), q ∈ (1,2,3,4). Among them, the logarithm in In (·) of c l is used to avoid | ∞ | or extreme value 0, so as to ensure the effectiveness of the calculation results.…”

Section: Algorithm Description Of Intelligent Health Monitoring Devicesmentioning

confidence: 99%

“…Suppose that the monitoring result a l of any sports event in intelligent health monitoring and the input z in the planning algorithm (data structure x i in intelligent equipment devices [ 11 ], impact of intelligent technology on health monitoring x j , and dynamic x k of health monitoring), p is the proportion of monitoring data (structured monitoring data >70%, semistructured monitoring data >70%, unstructured monitoring data >70%), q is the processing method of monitoring data distortion (reconstruction = 1, coefficient order = 2, quantification = 3, eigenvalue = 4, clustering = 4), then c l is described as Inc l o , p , q , o ∈ (1,2,….., n ), p ∈ (1,2,3,4), q ∈ (1,2,3,4). Among them, the logarithm in In (·) of c l is used to avoid | ∞ | or extreme value 0, so as to ensure the effectiveness of the calculation results.…”

Section: Algorithm Description Of Intelligent Health Monitoring Devicesmentioning

confidence: 99%

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A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm

Zheng

Jiang

2022

Computational Intelligence and Neuroscience

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With the continuous development of the national economy and scientific productivity, urban construction and people’s living standards are also getting higher and higher. Although people enjoy increasingly convenient life, the demand for intelligence is getting higher and higher. Digital intelligent equipment has the functions of data collection, calculation and analysis, diagnostic and early warning, and communication functions. Analyze the status quo and existing problems of the development of intelligent equipment, as well as analyze and research key monitoring technologies in the use and development of digital intelligent equipment and provide optimal solutions for intelligent equipment hardware development requirements, software development, and model algorithms. Intelligent equipment monitoring is related to all aspects of people’s livelihood, and its intelligent development is related to the public role in this field in the future. Accurate results of monitoring can provide data support for schools, research institutions, the public, and the government. At the same time, it is also an important basis for formulating social policies. At present, the commonly used monitoring method usually adopts time series algorithm. Through literature review, it is found that the algorithm has the problem of distortion of correct data, which affects the accuracy of monitoring results. Based on the above reasons, this article combines the wavelet function with the planning algorithm and proposes a dynamic programming algorithm, which removes the redundant monitoring data in turn and clusters the distortion monitoring data with the wavelet function, which improves the accuracy and computational efficiency of the algorithm and gives full play to the monitoring of intelligence. The simulation results using MATLAB show that the planning algorithm can eliminate 90% of redundant monitoring data and improve the extraction rate of characteristic monitoring data. At the same time, the accuracy of the planning algorithm reaches 95%, and the calculation time is less than 25 s, which is better than the static planning algorithm. Therefore, the dynamic programming algorithm can better utilize the intelligence, convenience, and efficiency of the equipment to optimize the monitoring model.

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Section: Algorithm Description Of Intelligent Health Monitoring Devicesmentioning

confidence: 99%

Section: Algorithm Description Of Intelligent Health Monitoring Devicesmentioning

confidence: 99%

A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm

Zheng

Jiang

2022

Computational Intelligence and Neuroscience

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“…For example, maritime container transport ships attach great importance to the security protection of the ship network system due to the relatively high value of their cargo ships. A dynamic network is adopted to protect network security [25,26]. In order to test whether the intelligent ship network real-time detection and protection strategy proposed in this paper can achieve the expected effect, relevant experiments were carried out after the algorithm was formed and the resulting data were statistically analyzed.…”

Section: Experiments Of Real-time Detection and Protection Of The Int...mentioning

confidence: 99%

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

Guo

2023

Symmetry

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When studying an unfamiliar system, we first look for the symmetry that the system has, so that we can make many predictions about the possible properties of the system. The symmetry in ship network security needs to maintain a stable state and maintain a constant state of ship network security. With the rapid development of network information technology, smart ships have become a new hot spot in the international shipping industry. The smart ships cybersecurity discussion is also at the top of the list in the maritime field. More and more shipping companies feel that their smart ship systems need to be upgraded and the main reason behind this is that the systems are maliciously attacked by cyber hackers. Therefore, it is extremely important to detect and protect the security of intelligent ship network systems in real time. The issue of network security has always accompanied the whole process of the development of the Internet. At the same time, with the development of Internet technology, network hacking attacks against the Internet have never stopped developing, and traditional ship network security risk detection and protection cannot achieve good results. After understanding the operation mode of intelligent ship networks, this paper deeply studied the characteristics of cloud computing technology and proposed a real-time risk detection method and protection strategy for intelligent ship network security based on cloud computing. This paper mainly used multi-sensor nodes to analyze data containing malicious attack information and implemented self-execution protection strategy generation nodes to intercept and protect from the attack, so as to achieve the purpose of maintaining the network security of intelligent ships. Through experiments, the virus intrusion detection and defense rate of the algorithm proposed in this paper was able to reach 85% to 95%, while the virus intrusion detection defense rate of the traditional intelligent ship network security protection algorithm was 55% to 65%. The detection rate of the algorithm proposed in this paper was able to reach 96.95% and the false positive rate was 2.56%. The detection rate of the traditional algorithm was only 70.76%, while the false positive rate reached 4.69%. All of the proposed algorithm’s data were significantly better than that of traditional algorithms, which proved that the performance of cloud computing-based real-time risk detection and protection algorithms for intelligent ship network security was significantly better than that of traditional algorithms.

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“…In the literature, genetic algorithm is preferred for solving many problems. Some of those; In solving the multi-mode multi-objective problem [18], it is used as a solution sequencing problem [18], in the solution of the effect maximization problem in social networks [19], in the solution of the dualobjective routing problem in dynamic networks [21], in the solution of the multi-objective reactive power distribution strategy problem for wind energy integrated systems [20], shape optimization [23], biomedicine [24]. Genetic algorithm is frequently preferred in the feature selection process, especially in recent years [26][27][28][29][30]…”

Section: Genetic Algorithmmentioning

confidence: 99%

Machine Learning-Based Comparative Study For Heart Disease Prediction

Gullu

Akçayol

Barışçı

2022

Advances in Artificial Intelligence Research

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Heart disease is one of the most common causes of death globally. In this study, machine learning algorithms and models widely used in the literature to predict heart disease have been extensively compared, and a hybrid feature selection based on genetic algorithm and tabu search methods have been developed. The proposed system consists of three components: (1) preprocess of datasets, (2) feature selection with genetic and tabu search algorithm, and (3) classification module. The models have been tested using different datasets, and detailed comparisons and analysis were presented. The experimental results show that the Random Forest algorithm is more successful than Adaboost, Bagging, Logitboost, and Support Vector machine using Cleveland and Statlog datasets.

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A customized genetic algorithm for bi-objective routing in a dynamic network

Cited by 18 publications

References 41 publications

A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm

A Mode of Intelligent Equipment Monitoring Optimization Based on Dynamic Programming Algorithm

Real-Time Risk Detection Method and Protection Strategy for Intelligent Ship Network Security Based on Cloud Computing

Machine Learning-Based Comparative Study For Heart Disease Prediction

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