A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization

Yu, Xiuwu; Zhou, Lixing; Li, Xiangyang

doi:10.1016/j.comnet.2019.02.011

Cited by 25 publications

(12 citation statements)

References 5 publications

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“…e CSO algorithm has been widely applied in various scientific and engineering domains. In the wireless sensor network (WSN) field, Fouad et al [25] used it to optimize the topological structure of the WSN, and Al Shayokh and Shin [26] and Yu et al [27] applied the CSO algorithm to handle the localization problem of the WSN. In the cloud computing domain, CSO has been used to solve deadlock-free migration of the virtual machine (VM) [28] and dynamic task scheduling problem [29].…”

Section: Optimization On Engineering Problemsmentioning

confidence: 99%

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

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The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and improve the convergence speed. We compare the FCSO with eight commonly used, state-of-the-art SIO algorithms in terms of performance in both low- and high-dimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical Friedman test. The results of the experiments on the 30 black-box optimization benchmarking (BBOB) functions demonstrate that our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm.

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Section: Optimization On Engineering Problemsmentioning

confidence: 99%

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Wang

Qin

Wan

et al. 2021

Mathematical Problems in Engineering

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“…The CSO algorithm is a biologically inspired metaheuristic algorithm, which imitates swarm foraging [34,35]. The CSO algorithm includes roosters, hens, and chicks.…”

Section: The Chicken Swarm Optimization Algorithm (Cso)mentioning

confidence: 99%

“…Shayokh and Shin [34] proposed an improved CSO algorithm to solve a wireless sensor network node localization problem. Yu et al [35] used CSO algorithm for deep well monitoring. The complexity of CSO algorithm can be reduced by the clustering method of wheel graph theory.…”

Section: Introductionmentioning

confidence: 99%

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

Liu

Tseng

et al. 2019

Applied Sciences

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In photovoltaic and wind power storage systems, the reliability of the battery directly affects the overall reliability of the energy storage system. Failed batteries can seriously affect the stable operation of energy storage systems. This paper aims to improve the reliability of the storage systems by accurately predicting battery life and identifying failing batteries in time. The current prediction models mainly use artificial neural networks, Gaussian process regression and hybrid models. Although these models can achieve high prediction accuracy, the computational cost is high due to model complexity. Least squares support vector machine (LSSVM) is a computationally efficient alternative. Hence, this study combines the improved chicken swarm optimization algorithm (ICSO) and LSSVM into a hybrid ICSO-LSSVM model for the reliability of photovoltaic and wind power storage systems. The following are the contributions of this work. First, the optimal penalty parameter and kernel width are determined. Second, the chicken swarm optimization algorithm (CSO) is improved by introducing chaotic search behavior in the hen and an adaptive learning factor in the chicks. The performance of the ICSO algorithm is shown to be better than CSO using standard test problems. Third, the prediction accuracy of the three models is compared. For NMC1 battery, the predicted relative error of ICSO-LSSVM is 0.94%; for NMC2 battery, the relative error of ICSO-LSSVM is 1%. These findings show that the proposed model is suitable for predicting the failure of batteries in energy storage systems, which can improve preventive and predictive maintenance of such systems.

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“…Barioli [25] studied the complete matrix of book graphs. Based on wheel graph and chicken swarm optimization, Yu et al [26] proposed a novel hybrid localization scheme for deep mine. Liu et al [27] proved that multifan graphs were determined by their Laplacian spectra.…”

Section: Introductionmentioning

confidence: 99%

On Subtree Number Index of Generalized Book Graphs, Fan Graphs, and Wheel Graphs

Sun

Zhao

et al. 2021

Journal of Mathematics

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With generating function and structural analysis, this paper presents the subtree generating functions and the subtree number index of generalized book graphs, generalized fan graphs, and generalized wheel graphs, respectively. As an application, this paper also briefly studies the subtree number index and the asymptotic properties of the subtree densities in regular book graphs, regular fan graphs, and regular wheel graphs. The results provide the basis for studying novel structural properties of the graphs generated by generalized book graphs, fan graphs, and wheel graphs from the perspective of the subtree number index.

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A novel hybrid localization scheme for deep mine based on wheel graph and chicken swarm optimization

Cited by 25 publications

References 5 publications

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

An Adaptive Fuzzy Chicken Swarm Optimization Algorithm

Improving the Reliability of Photovoltaic and Wind Power Storage Systems Using Least Squares Support Vector Machine Optimized by Improved Chicken Swarm Algorithm

On Subtree Number Index of Generalized Book Graphs, Fan Graphs, and Wheel Graphs

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