Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

Enhanced Arithmetic Optimization Algorithm Guided by a Local Search for the Feature Selection Problem

High-dimensional datasets present significant challenges for classification tasks.Dimensionality reduction,a crucial aspect of data preprocessing,has gained substantial attention due to its ability to improve classification per-formance.However,identifying the optimal features within high-dimensional datasets remains a computationally demanding task,necessitating the use of efficient algorithms.This paper introduces the Arithmetic Optimization Algorithm(AOA),a novel approach for finding the optimal feature subset.AOA is specifically modified to address feature selection problems based on a transfer function.Additionally,two enhancements are incorporated into the AOA algorithm to overcome limitations such as limited precision,slow convergence,and susceptibility to local optima.The first enhancement proposes a new method for selecting solutions to be improved during the search process.This method effectively improves the original algorithm’s accuracy and convergence speed.The second enhancement introduces a local search with neighborhood strategies(AOA_NBH)during the AOA exploitation phase.AOA_NBH explores the vast search space,aiding the algorithm in escaping local optima.Our results demonstrate that incorporating neighborhood methods enhances the output and achieves significant improvement over state-of-the-art methods.

Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.

Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-Hop Routing Protocol

Wireless Sensor Networks(WSN)has evolved into a key technology for ubiquitous living and the domain of interest has remained active in research owing to its extensive range of applications.In spite of this,it is challenging to design energy-efficient WSN.The routing approaches are leveraged to reduce the utilization of energy and prolonging the lifespan of network.In order to solve the restricted energy problem,it is essential to reduce the energy utilization of data,transmitted from the routing protocol and improve network development.In this background,the current study proposes a novel Differential Evolution with Arithmetic Optimization Algorithm Enabled Multi-hop Routing Protocol(DEAOA-MHRP)for WSN.The aim of the proposed DEAOA-MHRP model is select the optimal routes to reach the destination in WSN.To accomplish this,DEAOA-MHRP model initially integrates the concepts of Different Evolution(DE)and Arithmetic Optimization Algorithms(AOA)to improve convergence rate and solution quality.Besides,the inclusion of DE in traditional AOA helps in overcoming local optima problems.In addition,the proposed DEAOA-MRP technique derives a fitness function comprising two input variables such as residual energy and distance.In order to ensure the energy efficient performance of DEAOA-MHRP model,a detailed comparative study was conducted and the results established its superior performance over recent approaches.

A Comparison of Arithmetic Operations for Dynamic Process Optimization Approach

A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming(rSQP)simultaneous approach with respect to equality constrained optimization problems is presented.Through the detail comparison of arithmetic operations,it is concluded that the average iteration number within differential algebraic equations(DAEs)integration of quasi-sequential approach could be regarded as a criterion.One formula is given to calculate the threshold value of average iteration number.If the average iteration number is less than the threshold value,quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily.Two optimal control problems are given to demonstrate the usage of threshold value.For optimal control problems whose objective is to stay near desired operating point,the iteration number is usually small.Therefore,quasi-sequential approach seems more suitable for such problems.

An Optimal Double Inequality among the One-Parameter, Arithmetic and Geometric Means

In the present paper, we answer the question: for 0a what are the greatest value p(a) and the least value q(a) such that the double inequality Jp(a,b)aA(a,b)+ (1-a)G(a,b)Jq(a,b) holds for all a,b>0 with a is not equal to?b ?

Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems

This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.

Hybrid method for global optimization using more accuracy interval computation

In this paper, a novel hybrid method is presented for finding global optimization of an objective function. Based on the interval computation, this hybrid method combines interval deterministic method and stochastic evolution method. It can find global optimization quickly while ensuring the deterministic and stability of the algorithm. When using interval computation, extra width constraints accuracy of interval computation results. In this paper, a splitting method to reduce the extra width is introduced. This method is easy and it can get a more precise interval computation result. When finding the global optimization, it can increase the efficiency of pruning. Several experiments are given to illustrate the advantage of the new hybrid method.

基于改进仿射算法的主动配电网区间调度

针对高渗透率分布式可再生能源的不确定性和仿射算法结果的保守性影响调度计划的问题,提出基于改进仿射算法的主动配电网区间优化调度模型及其求解方法。以区间变量表征分布式可再生能源中风机和光伏出力的不确定性,利用带有误差修正机制的灰色马尔可夫模型得到风机和光伏出力的区间预测值。建立综合考虑主动配电网运行约束和灵活性指标,以综合运行费用最低和净负荷波动最小为目标的主动配电网多目标区间优化调度数学模型。在仿射算法的非线性运算中引入区间泰勒公式,提出一种改进仿射算法并将其应用于主动配电网潮流计算,并通过CPLEX和INTLAB对调度模型进行联合求解。修改的IEEE 33节点系统的仿真结果表明,区间优化调度可为调度人员提供更直观的主动配电网状态量上、下界信息,而且所提方法的计算效率更高,所得区间结果的保守性、可靠性和有效性更优。

优化聚类分簇结合自适应中继策略的双簇首WSNs路由算法

针对无线传感器网络中分簇路由算法节点能量利用率低、能量消耗不均匀等问题,提出了一种优化聚类分簇结合自适应中继策略的双簇首无线传感器网络路由算法.该算法对分簇路由协议中的三个阶段分别进行优化设计.成簇阶段,首先对双簇首模型下最优成簇规模与网络能耗的关系进行理论分析,然后使用改进的算术优化算法计算模糊C均值算法的初始聚类中心,提高了模糊C均值算法聚类成簇的准确率和鲁棒性.簇首选举阶段,引入双簇首策略,以节点的位置、能量和中心度为影响因子,根据承担任务的不同分别为内外簇首设计独立的簇首评价函数,以评价值为依据由节点分布式动态选举簇首减少了广播数量,同时可以将整个簇的能量负载平均分配到每个簇成员节点中.数据传输阶段,设置了多跳中继策略的距离适用条件,并以能量消耗速率为依据选择中继节点,避免了节点提前过载.仿真结果表明:在多种规模的网络中,该算法相较于对比算法在均衡网络负载、提高能量利用效率方面效果更好,从而延长了网络的有效感测时间.

一种引入过渡阶段和高斯变异的改进算术优化算法

针对算术优化算法收敛精度低、易陷入局部最优等问题,提出了一种改进的过渡高斯算术优化算法,该算法将新的非线性过渡阶段与改进的高斯变异策略相结合.首先,为了更好地从勘探阶段的高离散度策略过渡到开发阶段的低离散度策略,提出过渡阶段策略,并通过比较三种曲线实验重构数学优化加速函数.其次,引入具有算术优化算法特性的高斯变异策略和边界函数策略,加强算法跳出局部区域的能力.最后,将改进后的算术优化算法与几种著名算法进行对比,并进行不同维度的可扩展性分析,验证了所提算法的有效性.此外,该算法在压力容器设计问题中进行了测试.实验结果表明,TGAOA具有优异的收敛精度、收敛速度和鲁棒性.

基于IAOA-SVM模型结构时变可靠性研究

目的为有效解决使用传统代理模型进行结构时变可靠性研究中存在流程复杂、计算效率低等问题。方法提出以改进算术优化算法(Improved Arithmetic Optimization Algorithm,IAOA)优化支持向量机模型(Support Vector Machine,SVM)进行时变可靠性研究的方法,结合IAOA-SVM模型和极值理论,以某塔式起重机回转支承为研究对象,对其进行动态确定性分析获取样本数据,建立IAOA-SVM可靠性模型,采用蒙特卡洛法求解得到其可靠度结果,并与EKM和ERSM算法对比分析其仿真精度和效率。结果当回转支承径向变形许用值为0.278×10^(-3)m时,采用蒙特卡洛法求解得到其可靠度为99.68%,IAOA-SVM模型相比EKM和ERSM方法仿真效率有所提升,建模精度分别提高了10.42%和9.23%。结论IAOA-SVM方法在建模和仿真精度与效率方面具有较明显的优势,IAOA-SVM方法为求解机构时变可靠度难题提供了一种新的解决思路。

基于约束区间算法的模糊优化问题的Karush-Kuhn-Tucker条件

主要研究带有不等式约束的模糊优化问题,利用截集构建了与原问题等价的区间值优化问题,基于约束区间算法(CIA)将所得区间值优化问题转为等价的非线性优化问题,从而达到了去模糊化的目的.首先,定义了带有模糊系数函数截集的导数,并利用Zadeh分解定理给出模糊函数的导数概念.其次,在正线性无关约束规范下,建立了模糊优化问题的Karush-Kuhn-Tucker(KKT)条件.最后,利用KKT条件求解具体的模糊优化问题.

基于AOA-LSTM方法工业机器人轴承剩余使用寿命预测

为了提高工业机器人运行过程中振动控制精度,设计了一种基于算数优化算法(AOA)改进长短时记忆网络(LSTM)方法工业机器人轴承剩余使用寿命预测方法,并采用RMSE和MAE指标对预测模型进行评估。研究结果表明:损失曲线随着迭代次数增加趋于稳定,评估证明方法取得较好的预测结果。AOA-LSTM与实际值预测结果拟合度更高,较小的预测误差证明了方法的有效性。该方法有助于提高工业机器人轴承的使用效率,为后续整机性能测试奠定基础。

基于时移多尺度波动散布熵和改进核极限学习机的水电机组故障诊断

水电在能源供给结构改革中承担重要角色,随着风、光、潮汐等新型能源的不断接入,水电机组的负荷运行范围不断加宽,导致水电机组发生事故的概率增加,因此,开展水电机组智能故障诊断研究具有十分重要的现实意义。本文针对水电机组振动信号中蕴含大量噪声信号,干扰故障诊断的问题,提出一种时移多尺度波动散布熵和改进核极限学习机相结合的水电机组故障诊断方法。首先,结合信息熵理论与时移思想,在多尺度波动散布熵的基础上,采用时移理论替代多尺度波动散布熵(MFDE)中传统的粗粒化过程,提出时移多尺度波动散布熵(TSMFDE),通过仿真实验,证明所提方法具有良好的时序长度鲁棒性、抗噪性及特征提取能力,解决了传统多尺度熵粗粒化不足的问题。然后,利用具有可移植性强、寻优能力强和收敛速度快等特征的算术优化算法(AOA)对核极限学习机(KELM)的正则化参数和核函数参数进行寻优,建立AOA-KELM分类器,解决了KELM超参数难以调节的问题。最终,通过转子试验台模拟实验,将TSMFDE提取的特征输入分类器中,完成模式识别工作。仿真结果表明,所提模型取得最高的诊断精度,达到了100.0%,相对于其他流行模型,本文所提模型展现了明显的优势,验证了所提模型的良好诊断精度。

基于算数优化算法的VMD-BiLSTM模型的松土装置工况识别

当对滚筒式残膜回收机的关键装置松土齿耙的应力应变进行实时监测时,所获得应力应变信号易受外部环境的干扰,难以从信号中识别壅土故障。针对该问题,通过ANSYS分析确定松土齿耙的应变监测部位,利用应变片对松土齿耙不同工况进行应变监测试验。基于监测数据,提出一种基于算数优化算法(AOA)的变分模态分解(VMD)—双向长短期记忆网络(BiLSTM)神经网络模型工况识别方法。首先,利用AOA对VMD模态分量的k值和惩罚因子α进行参数优化;然后,使用VMD对松土齿耙应变信号进行自适应分解;最后,根据皮尔逊系数将分解并重构后的信号输入BiLSTM网络中进行特征学习,实现松土齿耙的工况识别。结果表明,该方法实现对松土齿耙空载、正常工作、轻度壅土、严重壅土4种工况精准识别,且效果优于VMD-LSTM、BiLSTM、LSTM神经网络模型,识别准确率达到99.1%以上,有效提高松土齿耙工况识别的准确率。

基于Q学习算术优化算法的无人机三维航迹规划

针对传统方法求解无人机三维航迹规划易导致规划代价高、精度差和容易陷入局部最优的不足,提出基于Q学习算术优化算法的无人机三维航迹规划算法。为了提升算术优化算法的寻优精度,引入Circle混沌映射提高初始种群多样性和分布均匀性,引入Q学习根据个体状态自适应调整数学优化加速函数更新,均衡算法全局搜索与局部开发,设计最优解邻域扰动优化全局搜索能力。通过建立无人机三维航迹规划模型,将航迹规划转化为多目标函数优化问题,并利用改进算法求解无人机三维航迹规划,以综合考虑航迹代价、地形代价和边界代价的目标函数评估粒子适应度,对航迹规划迭代寻优。仿真实验结果表明,所提算法规划的航迹具有更低的总代价和适应不同复杂地形环境的稳定性。

追踪源荷实时变化的仿射最优电-气能流在线计算方法

随着新能源渗透率的逐步提高,电-气互联系统的运行态势更为复杂多变。为响应源荷的实时变化,该文提出一种仿射最优电-气能流在线计算方法。首先,利用泰勒展开与隐式有限差分法构建天然气动态模型,并采用流速基值自适应校正方法提高模型准确性。其后,利用仿射算术描述系统的源荷不确定性,将最优能流由确定域拓展至仿射域,构建仿射最优电-气能流模型。在此基础上,通过离线优化获取最优调度方案关于噪声元的仿射解析表达式,利用噪声元追踪源荷的实时变化,进而在线获取决策方案。所提方法依据即时信息快速决策,可满足实时调度需求。最后,通过算例分析验证了所提动态模型的准确性,表明所提在线方法的调度性能接近具有完备预测信息的最优调度。

基于AOA优化SVM的工业过程故障检测

为了提高工业生产过程故障检测的精度,保证产品的质量和生产过程的安全,提出了一种基于算术优化算法(arithmetic optimization algorithm,AOA)优化支持向量机(support vector machine,SVM)的故障检测方法。首先,对工业过程中产生的数据进行标准化处理;然后,将处理后的数据作为训练样本建立SVM模型,同时采用算术优化算法对支持向量机中的惩罚参数C和核函数参数g进行优化,通过多次迭代对模型进行训练,建立最佳故障检测模型;最后,将测试数据导入建立的故障检测模型中进行故障检测。将提出的AOA-SVM方法应用于田纳西-伊斯曼过程进行实验验证,并与传统SVM、灰狼优化算法优化的支持向量机(GWO-SVM)方法进行比较,该研究提出的模型具有更高的准确率。实验仿真结果表明,提出的AOA-SVM故障检测模型具有更好的表现。

非线性自适应分组灰狼算法

针对灰狼算法(grey wolf optimization, GWO)收敛速度慢、易陷入局部最优的问题,提出了一种非线性自适应分组灰狼算法(Nonlinear Adaptive Grouping Grey Wolf Optimization, NAGGWO)。首先,提出CPM映射生成初始种群,提高种群多样性;随后,提出一种“S”型非线性控制参数用来平衡算法的开采与探索能力;最后,采用自适应分组策略将狼群分为捕食组、游荡组和搜索组,对不同组灰狼个体分别采用改进的差分进化策略、随机反向学习策略以及算数优化算法(Arithmetic Optimization Algorithm, AOA)中的乘除算子进行位置更新,以改善GWO的收敛速度及精度。通过选取12个测试函数对NAGGWO进行仿真,结果表明在相同条件下,NAGGWO在求解低维问题和高维问题中相比其它算法都具有显著优势。