An Energy-Efficient Protocol Using an Objective Function & Random Search with Jumps forWSN

Wireless Sensor Networks(WSNs)have hardware and software limitations and are deployed in hostile environments.The problem of energy consumption in WSNs has become a very important axis of research.To obtain good performance in terms of the network lifetime,several routing protocols have been proposed in the literature.Hierarchical routing is considered to be the most favorable approach in terms of energy efficiency.It is based on the concept parent-child hierarchy where the child nodes forward their messages to their parent,and then the parent node forwards them,directly or via other parent nodes,to the base station(sink).In this paper,we present a new Energy-Efficient clustering protocol for WSNs using an Objective Function and Random Search with Jumps(EEOFRSJ)in order to reduce sensor energy consumption.First,the objective function is used to find an optimal cluster formation taking into account the ratio of the mean Euclidean distance of the nodes to their associated cluster heads(CH)and their residual energy.Then,we find the best path to transmit data from the CHs nodes to the base station(BS)using a random search with jumps.We simulated our proposed approach compared with the Energy-Efficient in WSNs using Fuzzy C-Means clustering(EEFCM)protocol using Matlab Simulink.Simulation results have shown that our proposed protocol excels regarding energy consumption,resulting in network lifetime extension.

AN ANALYSIS ABOUT BEHAVIOR OF EVOLUTIONARY ALGORITHMS:A KIND OF THEORETICAL DESCRIPTION BASED ON GLOBAL RANDOM SEARCH METHODS

Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstruction and evolution of the sampling distributions over the space of candidate solutions. Iterativeconstruction of the sampling distributions is based on the idea of the global random search of generationalmethods. Under this frame, propontional selection is characterized as a gobal search operator, and recombination is characerized as the search process that exploits similarities. It is shown-that by properly constraining the search breadth of recombination operators, weak convergence of evolutionary algorithms to aglobal optimum can be ensured.

Improvement of Pure Random Search in Global Optimization

In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.

Random Search Algorithm for the Generalized Weber Problem

In this paper, we consider the planar multi-facility Weber problem with restricted zones and non-Euclidean distances, propose an algorithm based on the probability changing method (special kind of genetic algorithms) and prove its efficiency for approximate solving this problem by replacing the continuous coordinate values by discrete ones. Version of the algorithm for multiprocessor systems is proposed. Experimental results for a high-performance cluster are given.

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Research on stock trend prediction method based on optimized random forest

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis.Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock trend.This study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction.This study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random forest.As the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as input.Then,the parameter combination of the model is optimized through random parameter search.The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine model.Combined with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

Random Search and Code Similarity-Based Automatic Program Repair

In recent years,automatic program repair approaches have developed rapidly in the field of software engineering.However,the existing program repair techniques based on genetic programming suffer from requiring verification of a large number of candidate patches,which consume a lot of computational resources.In this paper,we propose a random search and code similarity based automatic program repair(RSCSRepair).First,to reduce the verification computation effort for candidate patches,we introduce test filtering to reduce the number of test cases and use test case prioritization techniques to reconstruct a new set of test cases.Second,we use a combination of code similarity and random search for patch generation.Finally,we use a patch overfitting detection method to improve the quality of patches.In order to verify the performance of our approach,we conducted the experiments on the Defects4J benchmark.The experimental results show that RSCSRepair correctly repairs up to 54 bugs,with improvements of 14.3%,8.5%,14.3%and 10.3%for our approach compared with jKali,Nopol,CapGen and Sim Fix,respectively.

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

Random walk search in unstructured P2P

Unstructured P2P has power-law link distribution, and the random walk in power-law networks is analyzed. The analysis results show that the probability that a random walker walks through the high degree nodes is high in the power-law network, and the information on the high degree nodes can be easily found through random walk. Random walk spread and random walk search method （RWSS） is proposed based on the analysis result. Simulation results show that RWSS achieves high success rates at low cost and is robust to high degree node failure.

Optimized quantum random-walk search algorithm for multi-solution search

This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.

Alternative Coins for Quantum Random Walk Search Optimized for a Hypercube

The present paper is focused on non-uniform quantum coins for the quantum random walk search algorithm. This is an alternative to the modification of the shift operator, which divides the search space into two parts. This method changes the quantum coins, while the shift operator remains unchanged and sustains the hypercube topology. The results discussed in this paper are obtained by both theoretical calculations and numerical simulations.

Decoherence in optimized quantum random-walk search algorithm

This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative.

Quasi-Coordinate Search for a Randomly Moving Target

In this paper, we study the quasi-coordinated search technique for a lost target assumed to move randomly on one of two disjoint lines according to a random walk motion, where there are two searchers beginning their search from the origin on the first line and other two searchers begin their search from the origin on the second line. But the motion of the two searchers on the first line is independent from the motion of the other two searchers on the second line. Here we introduce a model of search plan and investigate the expected value of the first meeting time between one of the searchers and the lost target. Also, we prove the existence of a search plan which minimizes the expected value of the first meeting time between one of the searchers and the target.

Linear random search and engineering estimation of sinkage for launching carrier aircraft

A linear random search algorithm(LRSA) is developed to determine the critical value of takeoff weight limited to the safe flight track sinkage and an engineering estimation method(EEM) is proposed to calculate the sinkage of carrier aircraft launch in real time. Based on the analysis of free flight after leaving the carrier, the equations are established to participate into engineering estimation of flight track sinkage. Thanks to the proposed search algorithm, the maximum takeoff weight of carrier aircraft with safe catapult launch flight track sinkage is generated in few steps. The results of sinkage estimation and the search algorithm are in good agreement with that of aircraft catapult launch simulation. The main contribution of this manuscript is the establishment of simple and accurate engineering estimation for carrier aircraft launch flight track sinkage and the development of robust and efficient search algorithm for the critical value with safe catapult criteria.

Optimal Coordinated Search for a Discrete Random Walker

This paper presents the search technique for a lost target. A lost target is random walker on one of two intersected real lines, and the purpose is to detect the target as fast as possible. We have four searchers start from the point of intersection, they follow the so called Quasi-Coordinated search plan. The expected value of the first meeting time between one of the searchers and the target is investigated, also we show the existence of the optimal search strategy which minimizes this first meeting time.

Effects of systematic phase errors on optimized quantum random-walk search algorithm

This study investigates the effects of systematic errors in phase inversions on the success rate and number of iterations in the optimized quantum random-walk search algorithm. Using the geometric description of this algorithm, a model of the algorithm with phase errors is established, and the relationship between the success rate of the algorithm, the database size, the number of iterations, and the phase error is determined. For a given database size, we obtain both the maximum success rate of the algorithm and the required number of iterations when phase errors are present in the algorithm. Analyses and numerical simulations show that the optimized quantum random-walk search algorithm is more robust against phase errors than Grover＇s algorithm.

Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

基于K-GRU神经网络的采煤机记忆截割及优化

目的针对采煤机记忆截割不准确、自动化程度不高的问题,方法本文提出一种基于KGRU神经网络的采煤机记忆截割算法,此算法具有更适合处理长时序数据的特点,将算法与采煤机记忆截割结合起来,可以减少采煤过程中滚筒的损坏同时保护工人生命安全。该算法在深层门控循环单元(GRU)的输入端引入比例因子K,用比例因子K表现不同时刻数据的重要程度,以加强模型对长时序数据的记忆性,进而提高记忆截割精度。在模型训练阶段利用随机搜索算法(RS)对深层K-GRU神经网络的超参数选择进行优化,加快模型训练速度。结果实验中使用Python完成K-GRU模型构建与超参数优化,使用随机搜索算法可以在更短时间内得到超参数最优解,得到超参数epochs为317、batch_size为70的最优解共花费154 s,在最优解情况下计算模型对真实采煤数据预测的误差,得到K-GRU的loss值为0.0467、R2为0.9578、EVS为0.9656、ME为0.0833。结论最终表明,优化后的深层K-GRU模型在解释方差得分、最大误差和可决系数方面均优于SVM、KNN、LSTM、RNN和普通GRU模型,显著提高了采煤机记忆截割的适用性和准确性。

灌溉机器人全覆盖路径规划方法

灌溉机器人全覆盖行动的各个任务具有较为明显的空间并行性,随着全覆盖范围扩大,在对覆盖区域进行分解阶段,需要充分考虑将整个区域空间分解为哪些区域。但是,灌溉机器人受到视觉感知区域限制,准确匹配和衔接路块间最近端点的难度较大,导致局部路点的连通和线路衔接出现差错,难以有效全覆盖。为了有效解决这一问题,提出一种灌溉机器人全覆盖路径规划方法。通过快速搜索随机算法展开需要覆盖区域的边界检测,考虑视觉传感器的感知范围受限因素,采用灰度质心法展开区域视图边界提取,根据提取结果建立地图。在地图上建立线段序列,通过曼哈顿最小距离原则连接地图上的部分路径线段,形成多个弓形线路块。使用分治算法匹配和衔接各个弓形线路块间最近端点对,引入改进A*算法对全局以及局部路点的连通和线路衔接,实现灌溉机器人的全覆盖路径规划。实验结果表明:针对简单灌溉区域,该方法的路径重复率为0.041%,灌溉覆盖率为98.90%;针对复杂灌溉区域,该方法的路径重复率为0.017%,灌溉覆盖率为99.87%。这说明针对不同的灌溉环境,该方法均可以实现理想的路径规划,不仅可以最大限度地实现全覆盖,并有效地减少路径冗余程度,可以获取理想的灌溉机器人全覆盖路径规划方案。

基于卷积神经网络的图像数据增强优化策略研究

论文基于卷积神经网络模型,提出一种改进的自动化图像数据增强策略。针对原有策略搜索空间的离散化及模型训练过程中超参数优化不稳定的问题,通过降低搜索空间策略的复杂度、优化子网络模型的训练过程、选取更有效率的增强随机搜索算法实现超参数优化等方法对原有策略进行改进。并针对不同类型的数据集进行了验证,实验结果表明,论文提出的数据增强策略在CIFAR-10、CIFAR-100、ImageNet数据集上提升了图像分类的准确性,取得了先进的实验效果。