One of the most reliable deep learning approaches for image classification challenges is deep Conventional Conv neural networks (DCNNs);however, identifying the appropriate DCNN architecture for a given application ca...
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One of the most reliable deep learning approaches for image classification challenges is deep Conventional Conv neural networks (DCNNs);however, identifying the appropriate DCNN architecture for a given application can be quite challenging. This study focuses on finding the optimal DCNN architecture automatically using an improved version of the chimp optimization algorithm (ChOA). Three changes based on the baseline ChOA are developed to accomplish the objectives. As a first step, a digitized-based coding strategy is created, making it easier for chimp vectors to encode DCNN layers. Then, to achieve variable-length DCNNs, a disabled layer is recommended to cover some chimp vector dimensions. As a third contribution, a mechanism is developed to assess the fitness function using only a part of the dataset instead of the whole dataset. In order to assess the developed model's performance, the comparison is made against 23 classifiers, including the top state-of-the-art approaches, using nine benchmark image datasets. The proposed model presents the best performance in the Fashion dataset with an error percentage of 5.08, while it is the second-best model with 750 k parameters. Also, for other datasets, the experimental findings indicate that the suggested method's classification accuracy outperforms other benchmarks in 87 out of 95 investigations. This variable-length approach is the first effort of its kind, employing ChOA to evolve the architectures of DCNNs autonomously.
This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA's capabilities for exploration and exploitation...
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This paper presents an evolved chimp optimization algorithm (ChOA) that uses greedy search (GS) and opposition-based learning (OBL) to respectively increase the ChOA's capabilities for exploration and exploitation in addressing real practical engineering-constrained problems. In order to investigate the efficiency of the GSOBL-ChOA, its performance is evaluated by twenty-three standard benchmark functions, 10 benchmark functions from CEC06-2019, a randomly generated landscape, and 12 real practical Constrained optimization Problems (COPs-2020) from a wide variety of engineering fields, including power system design, synthesis and process design, industrial chemical producer, power -electronic design, mechanical design, and animal feed ratio. The findings are compared to those obtained using benchmark optimizers such as CMA-ES and SHADE as state-of-the-art optimization techniques and CEC competition winners;standard ChOA;OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. In order to perform a comprehensive assessment, three non-parametric statistical tests, including the Wilcoxon rank-sum, Bonferroni-Dunn and Holm, and Friedman average rank tests, are utilized. The top two algorithms are GSOBL-ChOA and CMA-ES, with scores of forty and eleven, respectively, among 27 mathematical functions. jDE100 obtained the highest score of 100 in the 100-digit challenge, followed closely by DISHchain1e+12, which achieved the highest possible score of 97, and GSOBL-ChOA obtained the fourth-highest score of 93. Finally, GSOBL-ChOA and CMA-ES outperform other benchmarks in five and four real practical COPs, respectively. The source code of the paper can be downloaded using the following link: https://***/ matlabcentral/fileexchange/119108-evolving-chimp-optimization-algorithm-by-weighted-opposition.(c) 2022 Elsevier B.V. All rights reserved.
Radial basis function (RBF) neural network is one of the most practical tools in underwater image processing problems. Considering the deficiencies of RBF neural networks, such as low accuracy, slow convergence rate, ...
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Radial basis function (RBF) neural network is one of the most practical tools in underwater image processing problems. Considering the deficiencies of RBF neural networks, such as low accuracy, slow convergence rate, and entrapment in local minima, we use the chimp optimization algorithm (ChOA) to tackle these deficiencies. Two benchmark underwater image datasets are used to evaluate the efficiency of the improved detector. Moreover, a dataset of experimental underwater images is created to test the RBF-ChOA detector's ability to handle large underwater image datasets. To have a comprehensive approximation, the designed detector is compared to the Harris hawks optimization (HHO), slime mold algorithm (SMA), Kalman filter (KF), and Henry gas solubility optimization (HGSO) approach in terms of the detection accuracy, entrapment in local minima, and the convergence rate. According to the results, the suggested method outperforms previous RBF-based recognizers and, on average, recognizes underwater items 1.91% better than that of the top benchmark model.
An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness a...
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An Internet of Things (IoT)-assisted Wireless Sensor Network (WSNs) is a system where WSN nodes and IoT devices together work to share, collect, and process data. This incorporation aims to enhance the effectiveness and efficiency of data analysis and collection, resulting in automation and improved decision-making. Security in WSN-assisted IoT can be referred to as the measures initiated for protecting WSN linked to the IoT. This article presents a Binary chimp optimization algorithm with Machine Learning based Intrusion Detection (BCOA-MLID) technique for secure IoT-WSN. The presented BCOA-MLID technique intends to effectively discriminate different types of attacks to secure the IoT-WSN. In the presented BCOA-MLID technique, data normalization is initially carried out. The BCOA is designed for the optimal selection of features to improve intrusion detection efficacy. To detect intrusions in the IoT-WSN, the BCOA-MLID technique employs a class-specific cost regulation extreme learning machine classification model with a sine cosine algorithm as a parameter optimization approach. The experimental result of the BCOA-MLID technique is tested on the Kaggle intrusion dataset, and the results showcase the significant outcomes of the BCOA-MLID technique with a maximum accuracy of 99.36%, whereas the XGBoost and KNN-AOA models obtained a reduced accuracy of 96.83% and 97.20%, respectively.
The optimal multi-degree reduction of ball Said-Ball curves is an unsolved and knotty important technique in computer aided design (CAD) and computer graphics (CG) and is potentially used in many engineering fields in...
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The optimal multi-degree reduction of ball Said-Ball curves is an unsolved and knotty important technique in computer aided design (CAD) and computer graphics (CG) and is potentially used in many engineering fields involving geometric modeling. In this paper, an improved chimp optimization algorithm (ICHOA, for short) is used to solve the degree reduction of BSB curves. Firstly, the multi-degree reduction of BSB curves is mathematically an optimization problem that can be efficiently dealt with by a swarm intelligence algorithm. In this regard, a novel enhanced version of CHOA called ICHOA, combined with the proportional weight, dimension learning-based hunting search and fractional order strategies, is developed to enhance its capability of jumping out of the local minima and improve the calculation accuracy of the native algorithm. Furthermore, the superiority of the ICHOA is verified by comparing it with standard CHOA, other improved CHOA and popular nature-inspired optimizationalgorithms on 23 classical benchmark functions, the CEC'17 test suite and five engineering optimization problems, respectively. Secondly, the optimization models of multi-degree reduction for the center curve and radius function of BSB curves are established, respectively;meanwhile, the proposed ICHOA is utilized to solve the established optimization models, and the optimal center curve and radius function with a minimum distance of the approximating BSB curves of lower degree are also obtained. Finally, experimental results illustrate the ability of the proposed ICHOA to effectively solve the optimization problems of multi-degree reduction of BSB curves in terms of precision, robustness, and convergence characteristics.
In the present epoch of computing, the world has changed from older conventional print media to social platform channels. Fake news articles have the prospects to handle the opinions of the public and so may harm huma...
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In the present epoch of computing, the world has changed from older conventional print media to social platform channels. Fake news articles have the prospects to handle the opinions of the public and so may harm human groupings. Therefore, it is necessary to explore the authenticity and credibility of the news flash being shared on the internet community. Hence, this research paper devises an efficient and robust fake news detection model, named Exponential chimp optimization algorithm (EChOA)-based Deep Neuro-Fuzzy Network (DNFN) for detecting fake news. The introduced model utilizes a MapReduce framework that includes the mapper and reducer phases for processing big data for detecting fake news. First phase of processing is the Mapper work, in which every input used in the database is processed and creates an intermediate key-value pair. In the reducer phase, the fusion of features is performed by arranging the features with the help of computing the optimal parameter and Rand similarity coefficient using a Deep Q Network (DQN). Here, the detection of fake news is obtained by DNFN, and the DNFN is done using implemented EChOA. The EChOA-based DNFN effectively generates robust and effective fake news detection performance by choosing the optimal feature subsets through feature fusion. The EChOA is designed by integrating the Exponential Weighted Moving Average (EWMA) and chimp optimization algorithm (ChOA). Moreover, the EChOA-based DNFN method outperformed various former fake news detection approaches and attains the highest performance based on the testing accuracy is 0.909, sensitivity is 0.937, and specificity is 0.891 using the FakeNewsNet dataset.
To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved chimp optimization algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as t...
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To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved chimp optimization algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images' three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images.
The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions,...
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The chimp optimization algorithm (ChOA) is a hunting-based model and can be utilized as a set of optimization rules to tackle optimization problems. Although ChOA has shown promising results on optimization functions, it suffers from a slow convergence rate and low exploration capability. Therefore, in this paper, a modified ChOA is proposed to improve the exploration and exploitation capabilities of the ChOA. This improvement is performed using greedy search and opposition-based learning (OBL), respectively. In order to investigate the efficiency of the OBLChOA, the OBLChOA's performance is evaluated by twenty-three standard benchmark functions, ten suit tests of IEEE CEC06-2019, randomly generated landscape, and twelve real-world Constrained optimization Problems (IEEE COPs-2020) from a variety of engineering fields, including industrial chemical producer, power system, process design and synthesis, mechanical design, power-electronic, and livestock feed ration. The results are compared to benchmark optimizers, including CMA-ES and SHADE as high-performance optimizers and winners of IEEE CEC competition;standard ChOA;OBL-GWO, OBL-SSA, and OBL-CSA as the best benchmark OBL-based algorithms. OBLChOA and CMA-ES rank first and second among twenty-seven numerical test functions, respectively, with forty and eleven best results. In the 100-digit challenge, jDE100 achieves the highest score of 100, followed by DISHchain1e + 12, and OBLChOA achieves the fourth-highest score of 93. In total, eighteen state-of-the-art algorithms achieved the highest score in seven out of ten issues. Finally, OBLChOA and CMA-ES achieve the best performance in five and four real-world engineering challenges, respectively.
chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Bec...
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chimp optimization algorithm (ChOA) is a meta-heuristic algorithm inspired by individual intelligence and sexual motivation during group hunting. It is designed to speed up the convergence of the optimal solution. Because of its simplicity and low computational cost, the algorithm has been widely used to solve complex global optimization problem. But in the process of searching, it is easy to fall into the local optima, and the balance between exploitation and exploration cannot be realized well. In this paper, an adaptive chimp optimization algorithm called AChOA is proposed. Firstly, the Tent chaotic map is firstly used to initialize the chimp population to obtain a better initial solutions and improve convergence precision. Secondly, adaptive non linear convergence factor and adaptive weight are introduced in the global search stage, and the parameters vary adaptively according to the number of iterations and population diversity, so as to improve the population diversity. Thirdly, the Levy flight strategy is introduced into the position update formula to mitigate the shortcomings of ChOA algorithm, such as finding the local optima rather than the global optima, and lack of balance between the exploitation and exploration process. Finally, a comparison with 10 famous algorithms on 19 benchmark functions of the solving accuracy and convergence speed of AChOA is presented. The results show that AChOA has the advantages of fast convergence speed, high solution accuracy.
This study proposes a Hybrid AlexNet-Extreme Learning Machine (ELM) approach for breast cancer diagnosis using mammography images. Batch normalization is applied to improve AlexNet's performance, and the chimp opt...
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This study proposes a Hybrid AlexNet-Extreme Learning Machine (ELM) approach for breast cancer diagnosis using mammography images. Batch normalization is applied to improve AlexNet's performance, and the chimp optimization algorithm (ChOA) is utilized to avoid sub-optimal solutions in ELM. The Nelder-mead simplex (NEMS) technique is then employed to enhance the convergence behavior of ChOA. The study's main contributions are the proposed hybrid model and the application of ChOA and NEMS techniques to improve the performance of ELM. The proposed model is evaluated using the CBIS-DDSM dataset, with wiener and CALHE filters used as preprocessors. The effectiveness of the classification is examined using five optimizationalgorithms, and several metrics. The outcomes demonstrate that CALHE filter offered the best performance overall, and AlexNet-BN-ELM-CHOA-NEMS was the most accurate of the five models, with a sensitivity of 96.03 %, a specificity of 94.60 %, and an overall accuracy of 95.32 %. The findings demonstrate the effectiveness of the proposed model in breast cancer diagnosis.
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