Application research of improved genetic algorithm based on machine learning in production scheduling

Guo, Kai; Yang, Mei; Zhu, Hai

doi:10.1007/s00521-019-04571-5

Cited by 29 publications

(11 citation statements)

References 19 publications

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“…In the algorithm, we assume that the occurrence of each word is independent of each other according to naive Bayes. erefore, ( 5) is transformed into (6) as follows: (6) where P(w|c) is calculated according to (3). Since the probability value is small, in order to avoid underflow in the calculation process, the logarithm function log is introduced, and ( 6) is transformed into (7) as follows.…”

Section: Use Naive Bayesian Algorithm Tomentioning

confidence: 99%

“…In view of this research problem, Cui et al proposed that deep learning has the characteristics of automation and mass, and the combination of vulnerability mining and artificial intelligence can effectively improve the defects of traditional methods that cannot be batch processing and modularization [5]. Guo et al proposed to combine artificial intelligence technology into each step of vulnerability mining, so as to simplify the human cost required by each step and save the time cost required by each step [6]. e final results show that the intelligent detection scheme can surpass the detection accuracy of existing commercial tools [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

Liu

2022

Mobile Information Systems

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In order to accurately predict the security situation of Internet of Things information network, a research method based on machine learning algorithm for security situational awareness of Internet of Things information network is proposed. The perception result is represented by the perception model, the sample data are preprocessed based on the linear discriminant analysis method, the sample data are optimized to obtain the combined features, and then the processed data are used as the training input of the RBF neural network to find out the mapping relationship with the network situation value, so as to quantify the security posture of the system. The results show that automatic discovery and classification of seven violations is achieved. Since the platform was launched in China Mobile, more than 65,000 suspected illegal IoT cards have been discovered, effectively monitoring and controlling the operation and behavior of IoT cards. The processing efficiency of IoT card violations has been increased by more than 20 times. After the system is put into use, the discovery of suspected illegal users and behaviors can be realized through full automation, and the process of analysis, confirmation, and disposal can be shortened to 2 hours, which effectively reduce the false alarm rate and reduce operator costs. In previous monitoring of IoT cards, the false-positive rate of illegal IoT cards was about 83%, while the false-positive rate of existing algorithms dropped to 20%.Monitoring shows that business abuse monitoring detects the highest proportion of illegal IoT cards, 59%; infractions of Internet of Things cards detected through Internet abuse monitoring accounted for 20%; the proportion of Internet of Things card with machine card separation is 8%; in the information security risk monitoring (including spam text messages and harassing phone calls), the number of illegal Internet of Things cards found is small, only 3% and 2%, respectively; other infractions, including unauthorized use in locations and user complaints, accounted for 8%. It can effectively improve the ability to discover illegal IoT cards, greatly improve the accuracy of judgment, and improve the efficiency of disposal. The comparison verifies that the method is reliable and effective in the security situation awareness of the Internet of Things information network. Using the Internet of Things information security management system software based on the machine learning algorithm, the system software suitable for anomaly data detection is trained by adjusting the main parameters of the algorithm, which improves the automation and intelligence degree of the system software.

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Section: Use Naive Bayesian Algorithm Tomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

Liu

2022

Mobile Information Systems

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“…Specifically, heuristics and evolutionary algorithms have been proposed as alternative solution approaches for the FCR problem [28]- [30] to improve the runtime. Genetic algorithms (GAs) are a meta-heuristic that has received a lot of attention in recent years and have been employed in solving a broad cross-section of mathematically complex problems in diverse areas, including operational research [31], healthcare management [32], agriculture [33], path planning [34], and robotics [35], to name a few.…”

Section: A Genetic Algorithmmentioning

confidence: 99%

A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators

2021

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In this paper, we present a novel approach that addresses the problem of large-scale network topology design and routing. There are research works that used exact methodologies based on Integer Linear Programming (ILP) models to develop potential solutions for this problem. However, this problem is computationally NP-hard, thus solving it is hugely demanding on computational power for large-scale networks, and in many cases, it is not even possible to generate a solution with a reasonable optimality gap. This paper presents a hybrid algorithm based on the Genetic Algorithm with efficiently designed genetic operators. This algorithm aims to design the topology of large-scale networks and generate a routing configuration for a set of predefined traffic demands on the networks while keeping the total cost of design and routing at a minimum. The results have been compared to an exact ILP model, a relaxed ILP model, and a customized GA as benchmarks for validation purposes. These comparisons showed that the proposed algorithm significantly outperforms the ILP solutions in all of the large-scale network configurations that were used as case studies.INDEX TERMS Genetic algorithm, large-scale network, optimal topology, routing.

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“…With the rapid development in data science, machine learning has become a widely used technique in many areas [1][2][3][4], such as computer science, electrical engineering, manufacturing, and transportation [5][6][7][8]. As a classical combinatorial optimization problem, scheduling is known for its practical value and nondeterministic polynomial-time (NP) hardness.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based scheduling: a bibliometric perspective

Cao

et al. 2021

IET Collab Intel Manufact

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In recent years, the rapid development of artificial intelligence and data science has given rise to the study of data driven algorithms in highly volatile systems. The scheduling of complex shop floor resources falls into such a category, which is often non‐linear in nature, time varying, multi‐objective, and subject to interruptions. Ergo, the machine learning‐based scheduling, has become a research hotspot and attracted the attention of many scholars. In the literature, the research methods employed in solving scheduling problems are based on various perspectives, such as mathematical programming, combinatorial optimization, and heuristic rules. However, due to the inherent complexity of the problem, many issues remain to be addressed. In particular, with the availability of production data, the progress of computing power, and the breakthrough in intelligent algorithms, a novel branch of data driven algorithms present great potential, for example, the deep learning and reinforcement learning‐based algorithms. To reveal the value of machine learning‐based scheduling methods, bibliometric analysis was conducted to analyse the relevant articles and documents from the year 1980 to 2019. Finally, the future research trend in the domain of machine learning‐based scheduling is considered and tips are provided for researchers as well as practitioners to find leading scientists for collaborations.

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Application research of improved genetic algorithm based on machine learning in production scheduling

Cited by 29 publications

References 19 publications

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

A Novel Genetic Algorithm-Based Methodology for Large-Scale Fixed Charge Plus Routing Network Design Problem With Efficient Operators

Machine learning‐based scheduling: a bibliometric perspective

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