MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing

Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO).

SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification accuracy.However,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search process.Therefore,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection problem.In this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective space.Secondly,an S-shaped transfer function is introduced to perform binary transformation on SCChOA.Finally,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection method.To evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are considered.Meanwhile,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for comparison.Experimental results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection problem.It is capable of maximizing the reduction in the number of selected features while maintaining a high classification accuracy.Furthermore,the results of statistical tests also confirm the significant effectiveness of this method.

Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)signals.The deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition processes.This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step.Besides,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG signals.In addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classification.Finally,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent.An extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

Miss Chimp Says No to Smoking

A chimpanzee(大猩猩)in the zoo in Xi'an,Shaanxi Province has given up smoking after 16 years with the help of her keepers(饲养员).The zookeepers,wor- ried about her health,helped 27-year-old“Ai Ai”off tobacco(烟草)by treating her with entertainment(娱乐)and tasty diets.

Recent Advances of Chimp Optimization Algorithm:Variants and Applications

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence methods.It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial search.Also,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable convergence.Therefore,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short time.Thus,in this review paper,several research publications using ChOA have been overviewed and summarized.Initially,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual framework.The main operations of ChOA are procedurally discussed,and the theoretical foundation is described.Furthermore,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled versions.The main applications of ChOA are also thoroughly described.The applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,etc.Evaluation of ChOA is also provided.The review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and clustering.As well,it is wealthy in research on health,environment,and public safety.Also,it will aid those who are interested by providing them with potential future research.

Hybrid Modified Chimp Optimization Algorithm and Reinforcement Learning for Global Numeric Optimization

Chimp Optimization Algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various do-mains.However,ChOA suffers from the weakness of the local search technique which leads to a loss of diversity,getting stuck in a local minimum,and procuring premature convergence.In response to these defects,this paper proposes an improved ChOA algorithm based on using Opposition-based learning(OBL)to enhance the choice of better solutions,written as OChOA.Then,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called RLOChOA.This way effectively avoids the algorithm falling into local optimum.The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems.Numerical results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

改进群体智能算法的无线传感器网络覆盖优化

为解决无线传感器网络(Wireless Sensor Networks,WSN)节点分布不均和随机部署中的低覆盖率问题,该文提出一种改进群体智能算法的无线传感器网络覆盖优化算法,即改进的黑猩猩优化和哈里斯鹰优化的混合优化算法(Improved Chimp Optimization and Harris Hawk Optimization Algorithm,ICHHO).该算法首先对黑猩猩优化算法(Chimpanzee Optimization Algorithm,ChOA)进行改进,使用Levy Flight来改善其探索阶段,然后设计一个更新的公式来计算猎物逃逸能量,作为开发和探索之间的选择因素.传感器节点随机部署后,将ICHHO在传感器节点上执行,按照改进策略更新个体位置信息,计算相应的适应程度,找到最优传感器位置,并根据传感器概率模型确定网络最优覆盖率.仿真结果验证了ICHHO对于解决WSN覆盖问题的适用性,与其他优化算法的对比结果显示,ICHHO在提高覆盖率方面优于其他算法.

基于改进黑猩猩优化算法的有源配电网重构

为了更好地解决有源配电网重构问题,笔者提出一种基于改进黑猩猩优化算法(improved chimp optimization algorithm,ICOA)的有源配电网重构方法。以系统网损、电压偏移指数和负荷均衡度作为优化目标,建立有源配电网多目标重构模型,并利用加权处理法将多目标转化为单目标。采用收敛系数非线性变化和小孔成像学习策略对黑猩猩优化算法(chimp optimization algorithm,COA)进行改进,得到了优化效果更好的ICOA,并利用ICOA对目标函数进行优化,通过算例分析对所提方法的有效性进行验证。结果表明,采用ICOA重构后的系统网损、电压偏移指数和负荷均衡度分别下降36.89%、56.82%和45.76%,有源配电网运行的经济性和稳定性全面提升。

AIDS Virus Came From Chimps, Experts Conclude

【选注者言:美国伯明翰亚拉巴马大学的研究人员1999年1月31日在芝加哥发表论文报告认定:目前世界上有3500万人受到感染的艾滋病病毒来自赤道西非的黑猩猩。黑猩猩携带这种病毒已经有数十万年的历史,但是这种动物并未因此而得病。因此弄清黑猩猩是如何战胜这种病毒的感染对于人类预防和治疗艾滋病具有非常重要的意义。今年美国疾病控制与预防中心的艾滋病专家提出,艾滋病毒从黑猩猩进人人体几乎可以肯定发生在西非。当地的人们为得到食物而杀死黑猩猩,这样就使得这种病毒得以传人人体蔓延开来。这种黑猩猩生活在喀麦隆、赤道几内亚、刚果和中非共和国一带,据认为人类的艾滋病就最先发生在这个地区。 本文的另一个重要信息是:Many viruses come from animals. Flu, for example, comes from ducks and pigs.】

基于改进的极限学习机光伏出力短期预测

针对传统极限学习机易陷入局部最优解的缺点以及环境变化导致光伏出力波动的特点,构建了一种基于自适应噪声完全集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)算法,结合黑猩猩优化算法优化极限学习机神经网络的光伏出力短期预测模型。首先利用CEEMDAN算法将影响光伏输出功率的关键环境因素序列进行分解,得到数据信号在不同时间尺度的局部特征,降低环境因素序列的非平稳性,然后将各分解子序列和光伏历史数据序列作为黑猩猩算法优化的极限学习机预测模型输入进行预测。最后,选用DKASC Solar Centre光伏电站数据集对不同预测模型进行验证对比。实例仿真结果表明,构建的改进光伏出力预测组合模型的各项指标预测效果更好,且适用不同环境的光伏发电预测。

改进黑猩猩算法和LSSVR-BiLSTM双尺度模型的短期风功率预测

为提高风功率预测精度,提出一种基于改进自适应白噪声完全集合经验模态分解(ICEEMDAN)、排列熵(PE)、改进黑猩猩优化算法(ICHOA)、最小二乘支持向量回归机(LSSVR)和双向长短时记忆(BiLSTM)网络相结合的短期风功率预测混合模型。通过ICEEMDAN将非平稳的原始风电序列分解为相对平稳的模态分量,并使用PE聚合来降低计算复杂度。分别将BiLSTM模型和LSSVR模型应用于高频分量和低频分量的预测。采用ICHOA用于优化模型的参数。将每个预测分量值叠加得出最终预测结果。算例分析结果表明,所提LSSVR-BiLSTM双尺度深度学习模型与其他模型相比,能更好地拟合风功率数据,具有较高的预测精度和可行性。

融合多策略的黄金正弦黑猩猩优化算法

针对黑猩猩优化算法(Chimp optimization algorithm,ChOA)存在收敛速度慢、精度低和易陷入局部最优值问题,提出一种融合多策略的黄金正弦黑猩猩优化算法(Multi-strategy golden sine chimp optimization algorithm,IChOA).引入Halton序列初始化种群,提高初始化种群的多样性,加快算法收敛,提高收敛精度;考虑到收敛因子和权重因子对于平衡算法勘探和开发能力的重要作用,引入改进的非线性收敛因子和自适应权重因子,平衡算法的搜索能力;结合黄金正弦算法相关思想,更新个体位置,提高算法对局部极值的处理能力.通过对23个基准测试函数的寻优对比分析和Wilcoxon秩和统计检验以及部分CEC2014测试函数寻优结果对比可知,改进的算法具有更好的鲁棒性;最后,通过2个实际工程优化问题的实验对比分析,进一步验证了IChOA在处理现实优化问题上的优越性.

基于生长神经气改进模糊神经网络的铝电解过程时序数据预测

针对传统预测模型因分析铝厂时序数据时历史数据量大而无法快速挖掘实时数据隐含的知识信息,导致预测效率低的问题,提出一种基于生长神经气改进模糊神经网络(GNG-ANFIS)全局高效的时序混合预测模型。该模型首先利用生长神经气动态跟踪采集到的时序数据来识别数据奇异点,进而筛选有效数据;然后利用改进后的黑猩猩算法对传统模糊神经网络进行优化;最后,结合铝电解生产过程中铝液杂质铁含量时序数据验证该模型的性能。实验结果表明,混合模型在减少训练时间的情况下仍能准确预测铁含量时序数据,验证了其可行性。

一种新型的柯西扰动黑猩猩优化算法

针对黑猩猩优化算法(ChOA)在寻过程中求解精度低、收敛速度慢以及易陷入局部极值点等问题,提出一种新型的柯西扰动黑猩猩优化算法(CP-ChOA).首先采用佳点集映射初始化种群,增加算法在初始阶段的多样性;然后利用变异的柯西算子和反向学习策略,对当前最优位置进行扰动变异并产生新解,以提高算法的收敛速度,避免算法在迭代初期陷入局部极值;最后使用单纯形法策略改善最差个体的位置,增强算法的局部开发能力.选取8个基准函数和部分CEC2014测试函数进行试验仿真,结果表明CP-ChOA算法较标准ChOA算法、改进的ChOA算法以及其他元启发式算法具有更好的寻优性能,并通过优化2个工程设计问题,验证了CP-ChOA算法在工程上的可行性.

混沌精英池协同教与学改进的ChOA及其应用

针对黑猩猩优化算法存在全局搜索能力弱、寻优精度低、收敛速度慢等问题,提出一种混沌精英池协同教与学改进的黑猩猩优化算法(chimp optimization algorithm improved by the elite chaos pool collaborative teaching-learning,ECTChOA)。采用混沌精英池策略生成初始种群,增强初始解的质量和种群的多样性,为算法全局寻优奠定基础;引入自适应振荡因子平衡ChOA的全局探索和局部开发能力;结合教与学优化算法的教学阶段和粒子群优化算法的个体记忆思想优化种群位置更新过程,提高算法的寻优精度和收敛速度。仿真实验将ECTChOA与标准ChOA、其他元启发式优化算法和最新改进ChOA在12个基准测试函数下进行寻优对比,实验结果与Wilcoxon秩和检验p值结果均表明所提改进算法具有更高搜索精度、更快的收敛速度和更好的鲁棒性。另外,将ECTChOA应用于机械工程设计案例中,进一步验证ECTChOA在实际工程问题中的可行性和适用性。

采用多策略改进黑猩猩算法的农业机器人路径规划

为缩短农业机器人全局路径规划的长度和时间并获得更好的安全路径,提出了一种采用多策略改进黑猩猩算法(MIChOA)的路径规划方法。首先,对传统的黑猩猩算法(ChOA)进行改进,在种群初始化阶段引入佳点集策略提高种群的多样性;其次,根据黑猩猩实际寻优过程提出正态随机余弦收敛因子策略,平衡了算法全局勘探与局部开发能力;然后,在算法陷入局部最优停滞时采用贪婪竞争机制的停滞扰动策略,加快算法跳出局部最优并准确找到全局最优解;最后,利用标准测试函数验证MIChOA算法的寻优性能,建立了具有111个障碍物的环境栅格地图开展仿真实验,将MIChOA算法应用于农业机器人路径规划,并与其他4种较为优秀的改进ChOA算法进行比较。结果表明:MIChOA算法在单峰和复杂多峰函数上均具有较高的寻优精度、稳定的收敛性和良好的鲁棒性;MIChOA算法的路径搜索性能优于其他4种改进ChOA算法,其中路径长度缩短了28.01%,寻优时间和迭代次数分别减少了54.58%和85.87%。

融合学习行为策略的改进黑猩猩优化算法

针对黑猩猩优化算法收敛速度慢、寻优精度低以及容易陷入局部最优的问题,提出融合学习行为策略的改进黑猩猩优化算法(modified chimp optimization algorithm,MChOA)。采用准反向学习策略更新种群,增加种群的多样性和随机性,提高算法全局搜索能力,同时避免算法陷入局部最优。基于黑猩猩学习行为策略,通过随机选择“模仿学习”算子或“情绪感应”算子更新黑猩猩个体位置,增强算法局部开发能力,加快算法的收敛速度。选取16个基准函数以及12个CEC2014进行仿真实验测试,结果表明MChOA与传统ChOA相比具有较高的求解精度和较好的寻优性能。通过两个工程设计问题的求解,证明了MChOA在实际工程问题上也具有较高的实际应用价值。

基于ICOA优化极限学习机的网安态势预测

针对传统网络安全态势预测准确率不高、效率差的问题,利用改进黑猩猩算法优化极限学习机训练并构建网安态势预测模型ICOA-ELM。引入Tent混沌种群初始化、收敛因子非线性递减和差分进化机制对COA算法的初始种群多样性、全局搜索与局部开发均衡性及跳离局部最优能力进行改进,提高算法寻优性能。为提高极限学习机的预测精度和泛化能力,利用ICOA算法迭代优化ELM的关键参数,构建ICOA优化极限学习机的网安态势预测模型ICOA-ELM。实验结果表明,ICOA-ELM的预测结果拟合度更高,具有更好的预测效率和稳定性。

改进黑猩猩优化算法在阈值图像分割中的应用研究

黑猩猩优化算法(Chimp Optimization Algorithm,ChOA)是根据猩猩群体狩猎行为构建的一种群智优化算法.针对该算法在寻优能力和收敛效率等方面的困扰,提出一种改进的黑猩猩优化算法(Modified ChOA,MChOA).在MChOA算法中,利用特殊混沌模型对种群进行初始化,在提高种群针对性的同时,提高算法的收敛效率,并在位置更新过程中引入单纯形法策略来对种群中较差个体进行优化,进一步提高了算法的全面搜索能力,避免过早陷入局部最优.为了验证算法改进后的效果,将模糊Kapur熵作为目标函数,将MChOA算法应用于阈值图像分割中,与改进的模糊灰狼优化算法(MDGWO)的图像分割效果对比,MChOA算法的图像分割效果更佳.

Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.