Optimization of Linear Consecutive-k-Out-of-n Systems with Birnbaum Importance Based Ant Colony Optimization Algorithm

Wang, Wei; Cai, Zhiqiang; Zhao, Jianhua; Si, Shubin

doi:10.1007/s12204-019-2125-z

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…This distributed method can improve the calculation speed of the algorithm and increase the operating efficiency [ 19 , 20 ]. Heuristic exploration [ 21 ]: when each ant finds a path, heuristic exploration is carried out according to the heuristic factor, which can prevent the result from falling into the local optimum, and is beneficial to obtaining the global optimum solution of the optimization problem [ 22 , 23 ]. …”

Section: Integration Of Green Innovation Capabilities Of Enterprisesmentioning

confidence: 99%

Integration of Green Innovation Capabilities of Enterprises Based on Ant Colony Optimization Algorithm

Yang

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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This article studies the integration and optimization of green innovation capabilities of enterprises. The integration model of enterprise green innovation capability provides the theoretical basis for cultivating enterprise green innovation capability. However, from the perspective of optimization, in the life cycle of the enterprise, how the enterprise should reasonably allocate resources and carry out strategic guidance to achieve the optimal green innovation capability of the enterprise is a question worth thinking about. This stage of research starts from different cycle stages of enterprise development, explores the adaptability and evolution of the integration model, and studies the optimization methods of strategies to achieve the goal of matching. This article adopts the ant colony optimization algorithm to support the research on the integration of green innovation capabilities of enterprises. The article describes the principle of the ant colony optimization algorithm in detail by using the combination of text description and formula. In addition, it analyzes the effect of the research through experiments. The results show that the corresponding CITC coefficients of each item of the Green Innovation Scale are between 0.518 and 0.850, which are all greater than the ideal value of 0.4.

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Section: Integration Of Green Innovation Capabilities Of Enterprisesmentioning

confidence: 99%

Integration of Green Innovation Capabilities of Enterprises Based on Ant Colony Optimization Algorithm

Yang

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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“…Here, the optimal arrangements obtained by the PA are presented. For example, the optimal arrangements of the systems with 𝑛 = 24 in the large-variance, middle-variance, and small-variance cases using the PA are as follows: 9,19,5,13,24,8,12,18,4,14,21,7,15,20,3,11,23,6,16,22,2,10,17) Thus, it can be confirmed that the optimal arrangements depend on (the variance of) the component reliabilities.…”

Section: 2mentioning

confidence: 89%

“…[6][7][8] (2) Meta-heuristic methods, such as genetic algorithms, 9-12 simulated annealing, 13,14 and ant colony optimization. 15,16 Some heuristic and meta-heuristic methods are based on the Birnbaum importance (B-importance) measure. Bimportance was introduced by Birnbaum 17 and remains one of the most widely studied importance measures in the context of Con∕𝑘∕𝑛:F systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to the results of the numerical experiments, the performance of the BIGA was superior to that of the BIGLS in large-scale systems. 11 Recently, Wang et al 16 developed an ant colony optimization algorithm based on B-importance. They concluded that its performance was superior to that of the BIGLS algorithm for large Con∕𝑘∕𝑛:F systems.…”

Section: Literature Reviewmentioning

confidence: 99%

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Branch‐and‐bound‐based algorithm for solving the multi‐type component assignment problem in a consecutive‐k$k$‐out‐of‐n$n$:F system

Nakamura

Yamamoto

2021

Quality & Reliability Eng

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With the increasing scale and complexity of real‐world systems, it is crucial to improve system reliability in order to minimize occurrences of system failure. To achieve this, several prior studies have considered the component assignment problem for consecutive‐k$k$‐out‐of‐n$n$:F system models. This problem was extended to consider a system comprising multiple component types. This multi‐type component assignment problem (MCAP) aims to find an optimal component arrangement that achieves the maximum system reliability under the assumption that a component should be assigned only to a position of the same component type. A branch‐and‐bound (B&B) ‐based algorithm was developed to solve the MCAP in a consecutive‐k$k$‐out‐of‐n$n$:F system. The main principle of the proposed algorithm is the incorporation of the pruning conditions in the standard B&B framework to reduce the number of candidates for the optimal arrangement. It is experimentally demonstrated that the proposed algorithm can significantly improve the efficiency of the enumeration method in solving the MCAP. The proposed algorithm can obtain the optimal arrangement of a large system if the number of types is relatively large. The findings of this study can help in improving the reliability of any system that can be expressed as a consecutive‐k$k$‐out‐of‐n$n$:F system with multiple component types.

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“…The higher the α value is, the higher the pheromone concentration is. The larger the β value is, the greater the influence of the current attribute of the covering sample on the ant's selection path [18]. In order to accelerate the convergence speed, it is necessary to initialize the information concentration, and the pheromones of all paths are equal at the initial time.…”

Section: Adaptive Ant Colony Classification Rule Miningmentioning

confidence: 99%

Scientific Impact of Sports on Human Health and Physique Based on Optimization Big Data Ant Colony Algorithm

2021

Wireless Communications and Mobile Computing

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With the continuous improvement of living standards, people began to pay more and more attention to sports, and the impact of sports on human health and physique has been paid more and more attention. This study mainly analyzes the scientific impact of sports on human health and physique under the background of big data. Firstly, the big data analytic hierarchy process is used to construct the comprehensive evaluation structure system of sports on human health and physique. Then, an improved big data adaptive ant colony classification rule algorithm is proposed. Finally, the performance evaluation and physical impact analysis of the improved big data algorithm are carried out. The results show that compared with other algorithms, ACA ∗ (ant colony algorithm) based on big data has more obvious advantages in stability, optimization ability, running time, and convergence speed and is more suitable for practical application. In general, the improvement of the physical fitness level of the association members in 2019 mainly depends on the results of the improvement of the physical fitness level. In the future, we need to strengthen physical exercise, change living habits and traffic habits, and other methods to optimize the overall physical fitness.

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Optimization of Linear Consecutive-k-Out-of-n Systems with Birnbaum Importance Based Ant Colony Optimization Algorithm

Cited by 9 publications

References 17 publications

Integration of Green Innovation Capabilities of Enterprises Based on Ant Colony Optimization Algorithm

Integration of Green Innovation Capabilities of Enterprises Based on Ant Colony Optimization Algorithm

Branch‐and‐bound‐based algorithm for solving the multi‐type component assignment problem in a consecutive‐k$k$‐out‐of‐n$n$:F system

Scientific Impact of Sports on Human Health and Physique Based on Optimization Big Data Ant Colony Algorithm

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