LSALO-BP: A Hybrid Model for Children Adulthood Height Prediction

The prediction of children's adult height is a common procedure in childhood endocrinology. Through the prediction of children's adult height, it is possible to find abnormalities in children's growth and development. Many jobs in today's society have certain requirements for height, so the accuracy of children adulthood height prediction is important for children. Current methods for predicting adult height of children have some shortcomings such as inaccurate accuracy. To deal with these problems, this paper analyzes the data collected by the Chinese children and adolescents' physical and growth health projects in primary and secondary schools in Zhejiang Province, and proposes a method for predicting adult height based on back propagation neural network (BPNN) with the body composition of children and adolescents as input. Since the BP algorithm has the risk of falling into local optimization, and we propose LSALO-BP model that incorporates the ant lion optimizer (LSALO) into the BP algorithm as location strategy to avoid local optimization. The improvements achieved by the ant lion algorithm are mainly reflected in: improving the ant's walk mode, and enhancing the global search ability of the LSALO algorithm. The comparison experiment of 10 benchmark functions proves the feasibility and effectiveness of the location strategy. The LSALO-BP model is applied to the prediction of adult height of children and adolescents. The experimental results show that compared with other models, the LSALO-BP prediction model has increased the prediction accuracy by 6.67%~16.08% for boys and 4.67%~6.6% for girls, which can more accurately predict the adult height of children and adolescents.

Parameter Optimization of Active Disturbance Rejection Controller Using Adaptive Differential Ant-Lion Optimizer

Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers.

Exponential Ant-Lion Rider Optimization for Privacy Preservation in Cloud Computing

The innovative trend of cloud computing is outsourcing data to the cloud servers by individuals or enterprises. Recently, various techniques are devised for facilitating privacy protection on untrusted cloud platforms. However, the classical privacy-preserving techniques failed to prevent leakage and cause huge information loss. This paper devises a novel methodology, namely the Exponential-Ant-lion Rider optimization algorithm based bilinear map coefficient Generation (Exponential-AROA based BMCG) method for privacy preservation in cloud infrastructure. The proposed Exponential-AROA is devised by integrating Exponential weighted moving average (EWMA), Ant Lion optimizer (ALO), and Rider optimization algorithm (ROA). The input data is fed to the privacy preservation process wherein the data matrix, and bilinear map coefficient Generation (BMCG) coefficient are multiplied through Hilbert space-based tensor product. Here, the bilinear map coefficient is obtained by multiplying the original data matrix and with modified elliptical curve cryptography (MECC) encryption to maintain data security. The bilinear map coefficient is used to handle both the utility and the sensitive information. Hence, an optimization-driven algorithm is utilized to evaluate the optimal bilinear map coefficient. Here, the fitness function is newly devised considering privacy and utility. The proposed Exponential-AROA based BMCG provided superior performance with maximal accuracy of 94.024%, maximal fitness of 1, and minimal Information loss of 5.977%.

LIFETIME MAXIMIZATION OF A MOBILE WSN USING ZRP-FUZZY CLUSTERING PROTOCOL BASED ON ANT-LION OPTIMIZER

Wireless Sensor Networks require energy-efficient protocols for communication and data fusion to integrate data and extend the lifetime of the network. An efficient clustering algorithm for sensor nodes will optimize the energy efficiency of WSNs. However, the clustering process requires additional overhead, such as selection of cluster head, cluster creation, and deployment. This paper prepared a modified ZRP for mobile WSN clustering scheme and optimization using ant-lion optimization algorithm and so far named as mobility cluster head fuzzy logic based on the zone routing protocol (ZRP-FMC-ALO). Which proposed fuzzy logic approach based on three descriptors node for the selection of the CH nodes such as, residual energy, the concentration, and the centrality of the node and also exploited the concept of the mobility of the Base Station (BS) to prolong the life span of a WSN. The performance of the proposed protocol compared with the famous protocol such as LEACH. Using the MATLAB simulator and the result shows that it outperforms in terms of the WSN network lifetime, the average remaining-consuming energy, and the number of a live node.

Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.