Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization

Shayokh, Al; Shin, Soo Young

doi:10.1007/s11277-017-4803-1

Cited by 56 publications

(20 citation statements)

References 17 publications

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“…Compared with the classical optimizer such as particle swarm optimizer and ant swarm optimizer, it has stronger convergence ability and robustness. The CSO optimizer follows these rules (Al Shayokh and Shin, 2017;Fu et al, 2019;Tiana et al, 2017):…”

Section: Chicken Swarm Optimizer (Cso)mentioning

confidence: 99%

Power load forecasting in energy system based on improved extreme learning machine

Chen

Wun

et al. 2020

Energy Exploration & Exploitation

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Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

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Section: Chicken Swarm Optimizer (Cso)mentioning

confidence: 99%

Power load forecasting in energy system based on improved extreme learning machine

Chen

Wun

et al. 2020

Energy Exploration & Exploitation

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“…e learning factors φ 1 and φ 2 , appearing in the chick position update, indicate the degree of learning of chicks to hens and cocks to some extent, just like the individual learning factors and social learning factors in the particle swarm optimization [33]. From the learning factor in the particle swarm optimization, it can be inferred that, in the initial stage of the search, the larger φ 1 makes the chicks search for a greater probability around the hen and more is to find the optimal solution globally; in the later stages of the search, the larger φ 2 makes the chicks search for a greater chance around the cocks, which is a local search near the optimal solution [34]. is paper introduced a nonlinear learning factor that allowed chicks to perform local and global searches.…”

Section: Setting the Learning Factormentioning

confidence: 99%

“…In 2016, Hafez et al proposed a feature selection system based on chicken swarm optimization (CSO), which was used to select features in a wrapper mode to search for feature space for the best combination of features, thus maximizing classification performance while minimizing the number of selected features [20]. Shayokh et al used the chicken swarm optimization (CSO) to solve the wireless sensor network (WNS) node location problem [21]. In 2016, Roslina et al proposed an improved chicken swarm optimization (CSO) for ANFIS performance, which can more accurately solve the ANFIS network training classification problem [22].…”

Section: Introductionmentioning

confidence: 99%

Truss Structure Optimization Based on Improved Chicken Swarm Optimization Algorithm

Wang

Han

2019

Advances in Civil Engineering

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To improve the efficiency of the structural optimization design in truss calculation, an improved chicken swarm optimization algorithm was proposed for truss structure optimization. The chicken swarm optimization is a novel swarm intelligence algorithm. In the basic chicken swarm optimization algorithm, the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability. And the inertia weighting factor and the learning factor were introduced into the chick position update process, so as to better combine the global and local search. Finally, the overall individual position of the algorithm was optimized by the differential evolution algorithm. The improved algorithm was tested by multipeak function and applied to the truss simulation experiment. The study provided a new method for the truss structure optimization.

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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%

“…This technique has proven to be useful for many engineering applications. 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.…”

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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Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization

Cited by 56 publications

References 17 publications

Power load forecasting in energy system based on improved extreme learning machine

Power load forecasting in energy system based on improved extreme learning machine

Truss Structure Optimization Based on Improved 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

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