A New Crossover Mechanism for Genetic Algorithms for Steiner Tree Optimization

Zhang, Qiongbing; Yang, Shengxiang; Liu, Min; Liu, Jianxun; Jiang, Lei

doi:10.1109/tcyb.2020.3005047

Cited by 21 publications

(5 citation statements)

References 37 publications

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“…(2) Crossover operator P r : Based on the principle comparison, the model in this paper chooses the arithmetic crossover method [34][35][36]. Arithmetic crossover assumes that Γ t A , Γ t B is the arithmetic crossover between two sample individuals.…”

Section: Genetic Algorithm Optimizes the Weights And Resholds Of Neur...mentioning

confidence: 99%

Risk Assessment Method of Agricultural Management Investment Based on Genetic Neural Network

Zhao

2022

Security and Communication Networks

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At present, the economy has entered the new normal. In the environment of economic slowdown, agricultural operation and investment risk management is of great significance in investment activities. In order to avoid the related risks of the agricultural operation and investment, improve the stability of agricultural operation and investment risk assessment, and ensure the effect of the agricultural operation and investment risk assessment, an agricultural operation and investment risk assessment method based on genetic neural network is proposed. This method fully considers the global optimization capability of the genetic algorithm and optimizes the weights and thresholds of the BP neural network. According to the construction principle of the index system, from the perspective of systemic risk and nonsystematic risk, a risk assessment index system for agricultural business investment has been established. Based on the establishment of risk evaluation index system, using the genetic neural network method, the agricultural operation investment risk evaluation model is established to realize the agricultural operation investment risk evaluation. The experimental results show that the proposed method has a good effect on the risk assessment of the agricultural operation and investment and can effectively improve the stability of the risk assessment of agricultural operation and investment.

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Section: Genetic Algorithm Optimizes the Weights And Resholds Of Neur...mentioning

confidence: 99%

Risk Assessment Method of Agricultural Management Investment Based on Genetic Neural Network

Zhao

2022

Security and Communication Networks

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“…Besides, β is a binary vector which is generated randomly. Because the occurrence probability of crossover is typically much larger than that of mutation [65], the probability of crossover is set to 0.9 and the probability of mutation is set to 0.1. Considering the discrete mutation, the value of the mutation index is first set as 0.02 × v , and the mutation step is 0.7 × randn , where randn conforms to a normal distribution with mean 0 and variance 1, • and • represent the ceil and floor functions, respectively.…”

Section: Parameter Calibrationmentioning

confidence: 99%

An Angle-Based Bi-Objective Optimization Algorithm for Redundancy Allocation in Presence of Interval Uncertainty

Yang

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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Uncertainty is a practical issue in system design optimization because some characteristics of components, such as reliability and cost, cannot be determined precisely in many situations. Considering the imprecise characteristics of components, few works have focused on the multi-objective optimization for the redundancy allocation due to the challenges of comparing multi intervals. To tackle the issue, a novel angle-based bi-objective redundancy allocation algorithm is proposed in this study, introducing three original contributions: 1) An angle-based interval crowding distance (ICA) is especially designed for effective performance and reduced computational time; 2) Two techniques are applied to tackle the problem: An elite selection for mutation is presented for generating better offsprings; A penalty-guided constraint handling technique is introduced for converting the problem into an unconstrained one. 3) Since a set of optimal solutions is obtained by the proposed method and no preference on uncertainties is provided, this paper proposes a novel knee interval method to help DMs make a decision. To be specific, the proposed ICA can describe the distribution of the whole population intuitively and effectively, considering not only the angle between two compared individuals but also the angle range of the interval values. The computational results from two typical experiments demonstrate that the proposed algorithm is more efficient than other state-of-the-art algorithms, generating Pareto sets with less repeating individuals, stronger convergence, wider distribution, less imprecision, and reduced computational time.Note to Practitioners-This article is motivated by two practical problems in multi-objective redundancy allocation in presence of interval uncertainty: First, this paper tries to solve the multiobjective redundancy allocation problem with the imprecise characteristics of components, which is rarely considered in the field of reliability optimization design. Second, the calculation of the crowding distance needs extra time cost and is less efficient. To tackle this issue, an interval crowding angle is especially designed, considering not only the angle between two compared individuals, but also the angle range of the interval values. The proposed method can be embedded in most multi-objective

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“…Heuristic algorithms are the existing techniques used to solve signal timing optimization problems. The typical intelligent algorithms, such as genetic [27] and particle swarm optimization [28] algorithms, all have some drawbacks, such as ease of falling into a local optimum and slow operation efficiency. Thus far, many new intelligent optimization algorithms have been proposed to solve these problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

Cheng,

Qiao,

et al. 2023

Sensors

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With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

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A New Crossover Mechanism for Genetic Algorithms for Steiner Tree Optimization

Cited by 21 publications

References 37 publications

Risk Assessment Method of Agricultural Management Investment Based on Genetic Neural Network

Risk Assessment Method of Agricultural Management Investment Based on Genetic Neural Network

An Angle-Based Bi-Objective Optimization Algorithm for Redundancy Allocation in Presence of Interval Uncertainty

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm

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