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An enhanced chimp optimization algorithm for optimal degree reduction of Said-Ball curves

MATHEMATICS AND COMPUTERS IN SIMULATION 2022年第0期197卷 207-252页

作者： Hu, Gang Dou, Wanting Wang, Xiaofeng Abbas, Muhammad Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Univ Sargodha Dept Math Sargodha 40100 Pakistan

Because of its good geometric characteristics, Said-Ball curve has become a useful tool for shape design and geometric representation in product manufacturing. In this paper, an enhanced chimp optimization algorithm (CHOA, for short) is used to solve the problem of approximate multi-degree reduction of Said-Ball curve. Firstly, two strategies are used to improve the optimization performance of original CHOA, and an enhanced version of CHOA named SOCSCHOA combined with selective opposition and cuckoo search is presented. Furthermore, according to the idea of multi-degree reduction of Said-Ball curve, the problem of multi-degree reduction of Said-Ball curve is transformed into an optimization problem, and the presented SOCSCHOA is applied to the solutions of the optimization model of the problem. Finally, the approximate multi-degree reductions of Said-Ball curve with and without endpoint preserving interpolation are realized, and the errors of the degree reduction are also given, which is compared with the availability of degree reduction of other intelligent algorithms. Numerical examples provided show that the proposed method not only achieves a good effect of degree reduction, but also is easy to implement with high accuracy.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Said-Ball curves Degree reduction chimp optimization algorithm Selective opposition based learning Cuckoo search

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Hybrid chimp optimization algorithm for degree reduction of ball Said-Ball curves

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第9期56卷 10465-10555页

作者： Hu, Gang Dou, Wanting Wei, Guo Abbas, Muhammad Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Univ North Carolina Pembroke Pembroke NC 28372 USA Univ Sargodha Dept Math Sargodha 40100 Pakistan

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 optimization algorithms 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.

关键词： chimp optimization algorithm Dimension learning-based hunting search Fractional order strategy Ball Said-Ball curves Multi-degree reduction

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Underwater Image Detection and Recognition Using Radial Basis Function Neural Networks and chimp optimization algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第7期42卷 3963-3982页

作者： Tian, Ye Khishe, Mohammad Karimi, Rasoul Hashemzadeh, Esmail Azar, Omid Pakdel Shanxi Inst Technol Dept Big Data & Intelligent Engn Yangquan City Shanxi Peoples R China Imam Khomeini Marine Sci Univ Nowshahr Dept Marine Elect & Commun Engn Nowshahr Iran Imam Khomeini Marine Sci Univ Nowshahr Dept Mech Engn Nowshahr Iran Malek Ashtar Univ Technol Fac Naval Aviat Tehran Iran

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.

关键词： Underwater image processing Neural network Radial basis function chimp optimization algorithm

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Exponential chimp optimization algorithm based Deep Neuro-Fuzzy Network with MapReduce framework for fake news detection in big data analytics

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2023年第9期37卷 2414-2433页

作者： Kanchanamala, Pendela Rani, B. Selva Vairamuthu, S. GMR Inst Technol Dept Informat Technol Rajam Andhra Pradesh India Vellore Inst Technol Sch Informat Technol & Engn Vellore India Vellore Inst Technol Sch Comp Sci & Engn Vellore India

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.

关键词： big data chimp optimization algorithm Exponential Weighted Moving Average fake news social platform

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Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network

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ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2022年第3期111卷 403-417页

作者： Saffari, Abbas Khishe, Mohammad Zahiri, Seyed-Hamid Univ Birjand Dept Elect Engn Birjand Iran Imam Khomeini Marine Sci Univ Nowshahr Dept Marine Elect & Commun Engn Nowshahr Iran

chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging engineering problems. Due to the novelty of the ChOA, there is room for its improvement. Recognition and classification of marine mammals using artificial neural networks (ANNs) are high-dimensional challenging problems. In order to address this problem, this paper proposed the using of ChOA as ANN's trainer. However, evolving ANNs using metaheuristic algorithms suffers from high complexity and processing time. In order to address this shortcoming, this paper proposes the fuzzy logic to adjust the ChOA's control parameters (Fuzzy-ChOA) for tuning the relationship between exploration and exploitation phases. In this regard, we collect underwater marine mammals sounds and then produce an experimental dataset. After pre-processing and feature extraction, the ANN is used as a classifier. Besides, for having a fair comparison, we used a benchmark audio database of marine mammals. The comparison algorithms include ChOA, coronavirus optimization algorithm, harris hawks optimization, black widow optimization algorithm, Kalman filter benchmark algorithms, and also comparative benchmarks include convergence speed, local optimal avoidance ability, classification rate, and receiver operating characteristics (ROC). The simulation results show that the proposed fuzzy model can tune the boundary between the exploration and extraction phases. The convergence curve and ROC confirm that the convergence rate and performance of the designed recognizer are better than benchmark algorithms.

关键词： Fuzzy system chimp optimization algorithm Classification Neural network Marine mammals

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Multi-Level Image Segmentation Combining Chaotic Initialized chimp optimization algorithm and Cauchy Mutation

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Computers, Materials & Continua 2024年第8期80卷 2049-2063页

作者： Shujing Li Zhangfei Li Wenhui Cheng Chenyang Qi Linguo Li School of Computer and Information Engineering Fuyang Normal UniversityFuyang236041China

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 ***,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 ***,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 ***,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),*** experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

关键词： Image segmentation image thresholding chimp optimization algorithm chaos initialization Cauchy mutation

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Color Image Enhancement: A Metaheuristic chimp optimization algorithm

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NEURAL PROCESSING LETTERS 2022年第6期54卷 4769-4808页

作者： Du, Nating Luo, Qifang Du, Yanlian Zhou, Yongquan Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Hainan Univ Coll Informat & Commun Engn Haikou 570228 Hainan Peoples R China Hainan Univ State Key Lab Marine Resources Utilizat South Chi Haikou 570228 Hainan Peoples R China

The traditional incomplete Beta function has low parameter selection efficiency in image enhancement, and the adjustable range of parameters is small when stretching the area with low or high gray level, and the image enhancement with stretching at both ends and compression in the middle is almost ineffective. Therefore, an image enhancement method based on bilateral gamma correction and incomplete Beta function fusion is proposed. In this method, meta-heuristic chimp optimization algorithm is used to adaptively select the parameter values of the incomplete Beta function, bilateral gamma correction is used to further enhance the image contrast. In order to verify the effectiveness and feasibility of this method, 12 color images with contrast distortion and 15 contrast methods were selected. The experimental results show that our proposed method has good enhancement effect in avoiding over enhancement and mixing complex images with multiple attributes, and retains more image details. The color images enhancement effects are significantly better than other contrasted popular image enhancement methods.

关键词： Color image enhancement Incomplete Beta function Bilateral Gamma correction chimp optimization algorithm Meta-heuristic optimization

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An efficient multilevel thresholding segmentation method based on improved chimp optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 4693-4715页

作者： Fu, Xue Zhu, Liangkuan Wu, Bowen Wang, Jingyu Zhao, Xiaohan Ryspayev, Arystan Northeast Forestry Univ Coll Mech & Elect Engn Harbin Heilongjiang Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

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.

关键词： Multi-threshold color image segmentation chimp optimization algorithm particle swarm algorithm self-adaptive strategy Kapur's entropy

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An efficient binary chimp optimization algorithm for feature selection in biomedical data classification

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NEURAL COMPUTING & APPLICATIONS 2022年第8期34卷 6427-6451页

作者： Pashaei, Elnaz Pashaei, Elham Istanbul Aydin Univ Dept Software Engn Istanbul Turkey Istanbul Gelisim Univ Dept Comp Engn Istanbul Turkey

Accurate classification of high-dimensional biomedical data highly depends on the efficient recognition of the data's main features which can be used to assist diagnose related diseases. However, due to the existence of a large number of irrelevant or redundant features in biomedical data, classification approaches struggle to correctly identify patterns in data without a feature selection algorithm. Feature selection approaches seek to eliminate irrelevant and redundant features to maintain or enhance classification accuracy. In this paper, a new wrapper feature selection method is proposed based on the chimp optimization algorithm (ChOA) for biomedical data classification. The ChOA is a newly proposed metaheuristic algorithm whose capability for solving feature selection problems has not been investigated yet. Two binary variants of the ChoA are introduced for the feature selection problem. In the first approach, two transfer functions (S-shaped and V-shaped) are used to convert the continuous version of ChoA to binary. In addition to the transfer function, the crossover operator is utilized in the second approach to improve the ChOA's exploratory behavior. To validate the efficiency of the proposed approaches, five publicly available high-dimensional biomedical datasets, and a few datasets from different domains such as life, text, and image are employed. The proposed approaches were then compared with six well-known wrapper-based feature selection methods, including multi-objective genetic algorithm (GA), particle swarm optimization (PSO), Bat algorithm (BA), ant colony optimization (ACO), firefly algorithm (FA), and flower pollination (FP) algorithm, as well as two standard filter-based feature selection methods using three different classifiers. The experimental results demonstrate that the proposed approaches can effectively remove the least significant features and improve classification accuracy. The suggested wrapper feature selection techniques also outpe

关键词： chimp optimization algorithm Feature selection Biomedical data Classification optimization

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Improved chimp optimization algorithm for three-dimensional path planning problem

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第19期81卷 27397-27422页

作者： Du, Nating Zhou, Yongquan Deng, Wu Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Civil Aviat Univ China Coll Elect Informat & Automat Tianjin 300300 Peoples R China

The three-dimensional (3D) path planning of unmanned aerial vehicle (UAV) focuses on avoiding collision with obstacles and finding the optimal path to reach the target position in the complex environment. An improved chimp optimization algorithm (IChOA) based on somersault foraging strategy with adaptive weight was proposed to solve the three-dimensional path planning problem. Firstly, the position vector updating equation was dynamically adjusted by introducing the weighting factor derived from coefficient vector of the ChOA. Secondly, the somersault foraging strategy was introduced to prevent the group from falling into a local optimum in the later stage, and at the same time, the population diversity in the early stage was slightly improved. The algorithm was tested on CEC2019 functions and three-dimensional path planning. Compared with other methods, the results show that this algorithm can provide more competitive results.

关键词： Three-dimensional path planning chimp optimization algorithm Somersault foraging strategy Dynamic weighting factor Metaheuristic

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