Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

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%.

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).

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.

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.

Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy management.However,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of secrecy.Motivated by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the RL.By integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization algorithms.This can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the network.We compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network conditions.The proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence trend.For our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation dataset.This was accompanied by the lowest RMSE(0.95),indicating very precise predictions with minimal error.

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.