Design of Evolutionary Algorithm Based Energy Efficient Clustering Approach for Vehicular Adhoc Networks

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.Clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware Clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

Efficient AUV Path Planning in Time-Variant Underwater Environment Using Differential Evolution Algorithm

Robust and efficient AUV path planning is a key element for persistence AUV maneuvering in variable underwater environments. To develop such a path planning system, in this study, differential evolution(DE) algorithm is employed. The performance of the DE-based planner in generating time-efficient paths to direct the AUV from its initial conditions to the target of interest is investigated within a complexed 3D underwater environment incorporated with turbulent current vector fields, coastal area,islands, and static/dynamic obstacles. The results of simulations indicate the inherent efficiency of the DE-based path planner as it is capable of extracting feasible areas of a real map to determine the allowed spaces for the vehicle deployment while coping undesired current disturbances, exploiting desirable currents, and avoiding collision boundaries in directing the vehicle to its destination. The results are implementable for a realistic scenario and on-board real AUV as the DE planner satisfies all vehicular and environmental constraints while minimizing the travel time/distance, in a computationally efficient manner.

Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally disastrous.This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability programming.Inspired by evolutionary algorithms,which can iteratively find the nearoptimal solution,different Evolutionary Algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic Algorithm(GA),Evolution Strategy Algorithm(ES),Differential Evolution Algorithm(DE),and Evolutionary Programming Algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language.With the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms.It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

An Adaptive Differential Evolution Algorithm to Solve Constrained Optimization Problems in Engineering Design

Differential evolution (DE) algorithm has been shown to be a simple and efficient evolutionary algorithm for global optimization over continuous spaces, and has been widely used in both benchmark test functions and real-world applications. This paper introduces a novel mutation operator, without using the scaling factor F, a conventional control parameter, and this mutation can generate multiple trial vectors by incorporating different weighted values at each generation, which can make the best of the selected multiple parents to improve the probability of generating a better offspring. In addition, in order to enhance the capacity of adaptation, a new and adaptive control parameter, i.e. the crossover rate CR, is presented and when one variable is beyond its boundary, a repair rule is also applied in this paper. The proposed algorithm ADE is validated on several constrained engineering design optimization problems reported in the specialized literature. Compared with respect to algorithms representative of the state-of-the-art in the area, the experimental results show that ADE can obtain good solutions on a test set of constrained optimization problems in engineering design.

基于SPADE算法的梯级水库群联合防洪优化调度

针对梯级水库群联合防洪优化调度问题,提出一种基于自适应成功历史策略的改进差分进化算法(strategy and parameter adaptive differential evolution,SPADE)。该算法通过自适应成功历史差分策略来提升随机搜索效率,通过精英种群保守策略提升局部收敛速度及全局探索能力。据此开展包含10个测试函数的数值实验和赣江中游梯级水库群联合防洪优化调度实例,用于检验所提出的算法应用效果。结果表明:在数值实验中,SPADE算法收敛结果的最优值、平均值、标准差和成功次数评价指标整体优于SHADE、自适应差分进化算法(self-adaptive differential evolution,SADE)、遗传算法(genetic algorithm,GA)、粒子群算法(particle swarm optimization,PSO)、人工蜂群算法(artificial bee colony,ABC);在梯级水库群联合防洪优化调度实例应用中,通过对1964单峰和1973多峰型历史洪水过程进行分析,发现SPADE算法结果在削峰率指标上明显优于SADE、GA、PSO算法,且相比SHADE在两次历史洪水条件下的削峰率指标结果分别提升0.9%、3.4%。实验结果充分验证所提SPADE算法的优越性,可作为梯级水库群联合优化调度问题的有效求解工具。

Solving Ordinary Differential Equations with Evolutionary Algorithms

In this paper, the authors show that the general linear second order ordinary Differential Equation can be formulated as an optimization problem and that evolutionary algorithms for solving optimization problems can also be adapted for solving the formulated problem. The authors propose a polynomial based scheme for achieving the above objectives. The coefficients of the proposed scheme are approximated by an evolutionary algorithm known as Differential Evolution (DE). Numerical examples with good results show the accuracy of the proposed method compared with some existing methods.

FFDEZOA优化的SCARA机器人故障数据聚类分析

针对现有聚类方法对机器人故障数据聚类时对初始点选取依赖性大、收敛速度慢且精度低等问题,提出了一种FFDEZOA算法来对KMC聚类算法进行优化。ZOA算法具有寻优能力较强,收敛速度快,且在聚类时对初始点选取依赖性小,但其有几率会陷入到局部最优解。首先针对ZOA算法的缺点,提出了自由觅食策略、非线性收敛因子及斑马进化策略等来对其进行改进,能够有效提高算法搜索范围,从而避免局部最优;进而结合FFDEZOA和KMC算法的互补迭代,既加快了算法的搜索速度,也提升了精度。在多个公开数据集上的实验表明,FFDEZOA KMC在精确度和归一化互信息的指标上均优于ZOA KMC、AO KMC、KMC和MFO KMC,具有更好的收敛性能和聚类效果。最后依据各故障特征的主成分不同,利用FFDEZOA KMC对故障数据进行了聚类,可在多种工况下对机器人进行针对性的保养和维护。

User Purchase Intention Prediction Based on Improved Deep Forest

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

Parallel Evolutionary Modeling for Nonlinear Ordinary Differential Equations

We introduce a new parallel evolutionary algorithm in modeling dynamic systems by nonlinear higher-order ordinary differential equations (NHODEs). The NHODEs models are much more universal than the traditional linear models. In order to accelerate the modeling process, we propose and realize a parallel evolutionary algorithm using distributed CORBA object on the heterogeneous networking. Some numerical experiments show that the new algorithm is feasible and efficient.

Biological Network Modeling Based on Hill Function and Hybrid Evolutionary Algorithm

Gene regulatory network inference helps understand the regulatory mechanism among genes, predict the functions of unknown genes, comprehend the pathogenesis of disease and speed up drug development. In this paper, a Hill function-based ordinary differential equation (ODE) model is proposed to infer gene regulatory network (GRN). A hybrid evolutionary algorithm based on binary grey wolf optimization (BGWO) and grey wolf optimization (GWO) is proposed to identify the structure and parameters of the Hill function-based model. In order to restrict the search space and eliminate the redundant regulatory relationships, L1 regularizer was added to the fitness function. SOS repair network was used to test the proposed method. The experimental results show that this method can infer gene regulatory network more accurately than state of the art methods.

Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

Evolutionary Algorithm for Extractive Text Summarization

Text summarization is the process of automatically creating a compressed version of a given document preserving its information content. There are two types of summarization: extractive and abstractive. Extractive summarization methods simplify the problem of summarization into the problem of selecting a representative subset of the sentences in the original documents. Abstractive summarization may compose novel sentences, unseen in the original sources. In our study we focus on sentence based extractive document summarization. The extractive summarization systems are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document. In this paper, we propose unsupervised document summarization method that creates the summary by clustering and extracting sentences from the original document. For this purpose new criterion functions for sentence clustering have been proposed. Similarity measures play an increasingly important role in document clustering. Here we’ve also developed a discrete differential evolution algorithm to optimize the criterion functions. The experimental results show that our suggested approach can improve the performance compared to sate-of-the-art summarization approaches.

超参数自适应的MOEA/D-DE算法在翼型气动隐身优化中的应用

MOEA/D-DE算法易于实现,被广泛应用于处理多目标优化问题,但其超参数对算法性能影响较大。基于MOEA/D-DE算法框架,利用Sobol全局灵敏度分析方法对差分进化算子中的交叉控制参数进行改进,使用莱维飞行机制控制比例因子,使算法中的超参数拥有自适应能力,得到超参数自适应的MOEA/D-DE算法——MOEA/D-DEAH算法;对MOEA/D-DEAH算法、不同超参数设置的MOEA/D-DE算法和NSGAⅡ算法进行函数测试和翼型气动隐身优化算例对比。结果表明:MOEA/D-DEAH算法性能良好,具有较强的鲁棒性,气动隐身优化效果也比其他算法更好。

Web mining based on chaotic social evolutionary programming algorithm

With an aim to the fact that the K-means clustering algorithm usually ends in local optimization and is hard to harvest global optimization, a new web clustering method is presented based on the chaotic social evolutionary programming （CSEP） algorithm. This method brings up the manner of that a cognitive agent inherits a paradigm in clustering to enable the cognitive agent to acquire a chaotic mutation operator in the betrayal. As proven in the experiment, this method can not only effectively increase web clustering efficiency, but it can also practically improve the precision of web clustering.

A Clustering Method Based on Brain Storm Optimization Algorithm

In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.

Hybrid Improved Self-adaptive Differential Evolution and Nelder-Mead Simplex Method for Solving Constrained Real-Parameters

In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE （differential evolution） algorithm and exploitation capability of Nelder-Mead simplex method is presented （HISADE-NMS）. The DE has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles （NP）, scaling factor （F） and crossover control （CR）, which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depends on the characteristics of each objective function, therefore, we have to tune their value in each problem that mean it will take too long time to perform. In the new manner, we present a new version of the DE algorithm for obtaining self-adaptive control parameter settings. Some modifications are imposed on DE to improve its capability and efficiency while being hybridized with Nelder-Mead simplex method. To valid the robustness of new hybrid algorithm, we apply it to solve some examples of structural optimization constraints.

基于熵权-TOPSIS-DE模型对生产类原材料采购问题的研究

针对原材料订购与转运过程中的最优成本0-1规划问题,通过构建目标函数和约束条件,基于某建筑和装饰板材生产企业的基本情况,综合402家供应商的供货能力和转运商的转运损耗率,设计了一种基于熵权-TOPSIS-差分进化算法解决供应链采购问题的运输框架。一方面,对过去240周该企业原材料供应商的订货量和供货量进行了整体量化分析,选取供应商的订货量、供应商的订货次数、供应商的供应总量、供应稳定率、供货次数和平均供货量等一些评价指标,采用熵权-TOPSIS模型对所有供应商进行综合评价,最终筛选出108家优质供应商进行供货。另一方面,以企业是否选择该供应商作为决策变量,以采购费用最小为研究目标,通过差分进化算法,得到最优采购策略。同时以最低损耗率作为目标函数,通过差分进化算法得到最优运输方案。

慢性心力衰竭气虚血瘀证潜在亚组人群特征探索研究

目的探索慢性心力衰竭(CHF)气虚血瘀证潜在亚组人群,为心力衰竭的中医精准辨治提供客观依据。方法收集126例CHF气虚血瘀证患者的19种症状/体征指标和21种生化指标,利用R和Python软件进行K-mediods聚类算法分析,以轮廓系数确定最佳的聚类数目。组间比较,连续变量采用Kruskal-Wallis检验,分类变量采用Pearson's chi-squared检验或者Fisher's exact检验。结果CHF气虚血瘀证患者被聚类为3组,组间比较发现,气喘、不寐、颈脉怒张、纳差、畏寒5个症状/体征以及NYHA分级、氨基末端脑钠肽前体(NT-proBNP)、红细胞比容、尿素氮4个临床指标差异具有统计学意义(P<0.05)。第1组人群整体各症状/体征发生频率和临床指标严重程度相对较低;第2组人群以颈脉怒张发生频率较其他组高为特征,且促甲状腺激素、总胆固醇及低密度脂蛋白有偏高趋势,血小板有偏低的趋势;第3组人群以畏寒、纳差、不寐的发生率较其他组显著升高为特征,伴随尿素氮增高、红细胞比容降低。结论CHF气虚血瘀证潜在3种亚型,分别是气虚血瘀证本证型、气虚血瘀兼痰浊型、气虚血瘀兼阳虚型。

烘丝筒出口叶丝含水率预测模型研究

烘丝的最佳工艺参数难以确认,且叶丝含水率预测误差较大。为了在信息技术方面辅助提升烟草成品质量,研究基于极限学习机(ELM)的烘丝筒出口叶丝含水率预测模型。选取叶丝烘丝过程中松散回潮、预混柜、润叶加料等工艺阶段环境温度、湿度、加水比例等工艺参数。通过随机森林方法,将处理后有效数据中的各烘丝工艺参数以平均精准度逐渐减少顺序进行重新排序,筛选出对烘丝筒叶丝含水率预测作用较大的烘丝工艺参数。将筛选后的烘丝工艺参数作为ELM的输入数据,获取叶丝含水率预测结果。以含水率预测平均绝对误差最小为差分进化算法的适应度函数,优化ELM的隐含层神经元数量,提升烘丝筒出口叶丝含水率预测精度。试验结果表明,该模型可实现烘丝筒出口叶丝含水率预测,且预测误差小于0.3%,预测精度高。该研究有助于提升烟草质量。

混合整数优化问题的差分进化算法研究

为求解混合整数优化问题,提出了混合整数差分进化算法(Mixed Integer Differential Evolution,MIDE)。该算法结合整数变量的特点,为整数类型变量设计了专用的变异算子,使整数变量可以在差分进化算法中直接进化;为了维持种群多样性,采用了灾变策略;采用双编码方式,使整数变量与连续变量并行进化,进而提出了混合整数差分进化算法。通过与其它混合整数优化算法的比较,证明该算法具有较好的收敛速度、全局收敛性及算法稳定性等优点。