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Hybrid multi-strategy chaos somersault foraging chimp optimization algorithm research

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第7期20卷 12263-12297页

作者： Yang, Xiaoru Zhang, Yumei Lv, Xiaojiao Yang, Honghong Sun, Zengguo Wu, Xiaojun Shaanxi Normal Univ Key Lab Modern Teaching Technol Minist Educ Xian Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Peoples R China Serv Technol Folk Song Key Lab Intelligent Comp Xian Peoples R China

To address the problems of slow convergence speed and low accuracy of the chimp optimization algorithm (ChOA), and to prevent falling into the local optimum, a chaos somersault foraging ChOA (CSFChOA) is proposed. First, the cat chaotic sequence is introduced to generate the initial solutions, and then opposition-based learning is used to select better solutions to form the initial population, which can ensure the diversity of the algorithm at the beginning and improve the convergence speed and optimum searching accuracy. Considering that the algorithm is likely to fall into local optimum in the final stage, by taking the optimal solution as the pivot, chimps with better adaptation at the mirror image position replace chimps from the original population using the somersault foraging strategy, which can increase the population diversity and expand the search scope. The optimization search tests were performed on 23 standard test functions and CEC2019 test functions, and the Wilcoxon rank sum test was used for statistical analysis. The CSFChOA was compared with the ChOA and other improved intelligent optimization algorithms. The experimental results show that the CSFChOA outperforms most of the other algorithms in terms of mean and standard deviation, which indicates that the CSFChOA performs well in terms of the convergence accuracy, convergence speed and robustness of global optimization in both low-dimensional and high-dimensional experiments. Finally, through the test and analysis comparison of two complex engineering design problems, the CSFChOA was shown to outperform other algorithms in terms of optimal cost. For the design of the speed reducer, the performance of the CSFChOA is 100% better than other algorithms in terms of optimal cost;and, for the design of a three-bar truss, the performance of the CSFChOA is 6.77% better than other algorithms in terms of optimal cost, which verifies the feasibility, applicability and superiority of the CSFChOA in practical e

关键词： chimp optimization algorithm cat chaotic sequence opposition-based learning somersault foraging convergence local optimum

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Wind speed prediction by a swarm intelligence based deep learning model via signal decomposition and parameter optimization using improved chimp optimization algorithm

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ENERGY 2023年第1期276卷

作者： Suo, Leiming Peng, Tian Song, Shihao Zhang, Chu Wang, Yuhan Fu, Yongyan Nazir, Muhammad Shahzad Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China Huaiyin Inst Technol Jiangsu Permanent Magnet Motor Engn Res Ctr Huaian 223003 Peoples R China

Accurate prediction of wind speed plays a very important role in the stable operation of wind power plants. In this study, the goal is to establish a hybrid wind speed prediction model based on Time Varying Filtering based Empirical Mode Decomposition (TVFEMD), Fuzzy Entropy (FE), Partial Autocorrelation Function (PACF), improved chimp optimization algorithm (IChOA) and Bi-directional Gated Recurrent Unit (BiGRU). Firstly, the original wind speed data was decomposed by TVFEMD to obtain modal components, and FE aggregation is used to decrease the computational complexity. Secondly, the components are processed by PACF to extract important input features. Thirdly, the BiGRU parameters are optimized using IChOA which is an improved version of ChOA. Finally, the optimized BiGRU is used to predict the decomposed components, and the predicted components are summed to obtain the final prediction result. In this experiment, the proposed model is used to predict the data of four months of a year from Station 46,060 of National Data Buoy Center, and the performance of eight benchmark models is analyzed. Experimental results show that TVFEMD and PACF can improve the prediction accuracy of the model. IChOA is feasible to optimize the parameters of BiGRU and can improve the prediction performance.

关键词： Wind speed prediction TVFEMD chimp optimization algorithm BiGRU Deep learning

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A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems

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Iran Journal of Computer Science 2024年第1期7卷 65-101页

作者： Haeri Boroujeni, Sayed Pedram Pashaei, Elnaz School of Computing Clemson University Clemson 29634 SC United States Department of Medical and Molecular Genetics Indiana University School of Medicine Indianapolis United States

This paper focuses on connectivity-based data clustering for categorizing similar and dissimilar data into distinct groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. In this study, we attempt to conceptualize a powerful approach using the three main components: chimp optimization algorithm (ChOA), Generalized Normal Distribution algorithm (GNDA), and Opposition-Based Learning (OBL) method. First, two versions of ChOA with two different dynamic coefficients and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Second, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. In this study, eight benchmark datasets, including five datasets of the UCI machine learning repository and three challenging shape datasets, are used to investigate the general performance of the proposed method. The results are evaluated against several popular and recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the Sum of Intra-Cluster Distances (SICD), Error Rate (ER), and convergence rate. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： chimp optimization algorithm Data clustering Generalized normal distribution algorithm K-means Meta-heuristic optimization algorithm Opposition-based learning

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Diagnose Parkinson?s disease and cleft lip and palate using deep convolutional neural networks evolved by IP-based chimp optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Chen, Feng Yang, Chunyan Khishe, Mohammad Wenzhou Med Univ Fac Informat Wenzhou 325035 Zhejiang Peoples R China Ctr China Normal Univ Fac Artificial Intelligence Educ Wuhan 430079 Hubei Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran

Speech signals often include paralinguistic features such as pathologies that impair a speaker's capability to communicate. Those cognitive symptoms have various causes depending on the disease. For example, morphological diseases like cleft lip and palate create hypernasality, while neurodegenerative conditions like Parkinson's disease cause hypokinetic dysarthria. Automatic assessment of abnormal speech supports early diagnosis or disease severity evaluation. Conventional methods rely on manually assessing single aspects like shimmer, jitter, or formant frequencies, which may not fully reflect the disease's manifestations. In this paper, we use deep convolutional neural networks (DCNNs) to recognize disordered speech. Despite DCNNs' many approved benefits, selecting the best structure is challenging. In order to overcome this issue, this research looks into using the chimp optimization algorithm (ChOA) to automatically select the optimal DCNN structure. In order to achieve the goal, three ChOA-based advancements are proposed. First, an internet protocol address-based (IPA-based) encoding method for DCNN layers employing chimp vectors is created. Then an Enfeebled layer with specified chimp vector dimensions is presented for variable-length DCNNs. Eventually, large datasets are partitioned into smaller ones and evaluated at random to recognize abnormal speech signals from patients with Parkinson's disease and cleft lip and palate. In addition to receiver operating characteristic (ROC) and precision-recall curves, five well-known metrics were used: sensitivity, specificity, accuracy, precision, F1-Score. The proposed model accurately diagnoses disordered and normal speech signals, with an accuracy of up to 96.37%, which is 1.62 more accurate than the second-best approach, i.e., VLNSGA-II.

关键词： Pathological speech Deep convolutional neural networks Parkinson?s disease Cleft lip and palate chimp optimization algorithm

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

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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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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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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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Spiral Shape - chimp optimization algorithm for Secure Cluster-Based Routing in Wireless Sensor Network 2

Spiral Shape - Chimp Optimization Algorithm for Secure Clust...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vennam, Preethi Mouleeswaran, S.K. Dayananda Sagar University Department of Computer Science and Enigineering Bengaluru India

ISBN: (纸本)9798350317558

A Wireless Sensor Network (WSN) is a self-organization network that contains several tiny sensor nodes for tracking and monitoring an application in a wide range. Still, security and consumption of energy are two major threats due to open sources and restricted energy sources. In the research, the Spiral Shape-chimp optimization algorithm (SS-ChOA) method is proposed to perform secure cluster-based routing in WSN. The SS-ChOA is selected due to its high convergence rate and avoiding local optima in the exploitation phase. The proposed SS-ChOA is used to choose secure Cluster Heads (CHs) and routing for achieving secure communication across the network. The proposed algorithm provided maximized security against malicious nodes while maximizing energy efficiency. The performance of the SS-ChOA method is evaluated through Packet Delivery Ratio (PDR), throughput, End-to-end Delay (EED), and energy consumption. SS-ChO algorithm obtained less energy consumption of 0.35 J, 0.43 J, 0.57 J, 0.66 J, and 0.73 J by varying nodes from 100 to 500. The proposed algorithm performed more efficiently than other existing algorithms like Multi-objective Trust based Crow Search algorithm (MTCSA) - Hybrid Encryption algorithm (HEA), Multi Objective Trust Aware Hybrid optimization (MOTAHO), and Multi-Objective Trust Centric Artificial Algae algorithm (M-TCAAA). © 2024 IEEE.

关键词： chimp optimization algorithm cluster heads malicious nodes security spiral shape wireless sensor network

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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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