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A multi-strategy chimp optimization algorithm for solving global and constraint engineering problems

KNOWLEDGE AND INFORMATION SYSTEMS 2025年 1-50页

作者： Anka, Ferzat Fatih Sultan Mehmet Vakif Univ Fac Engn Comp Engn Dept Istanbul Turkiye Fatih Sultan Mehmet Vakif Univ Data Sci Applicat & Res Ctr VEBIM Istanbul Turkiye

The chimp optimization algorithm (ChOA) is a recently introduced metaheuristic algorithm inspired by nature. This algorithm identified four types of chimpanzee groups: attacker, barrier, chaser, and driver, and proposed a suitable mathematical model for them, based on the various intelligence and sexual motivations of chimpanzees. However, this algorithm is not more successful in the convergence rate and escaping of the local optimum trap in solving high-dimensional problems. Although it and some of its variants use some strategies to overcome these problems, it is observed that it is not sufficient. Therefore, in this study, a newly expanded variant is described. In the algorithm, called Ex-ChOA, hybrid models are proposed for position updates of search agents, and a dynamic switching mechanism is provided for transition phases. This flexible structure solves the slow convergence problem of ChOA and improves its accuracy in multi-dimensional problems. Therefore, it tries to achieve success in solving global, complex, and constrained problems. The performance of the proposed algorithm was analyzed on a total of 34 benchmark functions and a total of 17 real-world optimizations, including classical, constrained, and modern engineering problems. According to the results obtained, the proposed algorithm performs better or equivalent performance than the compared algorithms.

关键词： optimization Metaheuristic chimp optimization algorithm Engineering constrained problems

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A parameter estimation method for neural mass model based on the improved chimp optimization algorithm and Riemannian geometry

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CHAOS SOLITONS & FRACTALS 2025年 194卷

作者： Yan, Shaoting Shi, Xiaochu Li, Ruiqi Zhang, Lipeng Zhang, Rui Chen, Mingming Li, Meng Zhang, Hui Li, Runtao Shi, Li Hu, Yuxia Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou Peoples R China Henan Key Lab Brain Sci & Brain Comp Interface Te Zhengzhou Peoples R China Zhengzhou Univ Inst Neurosci Zhengzhou Peoples R China Zhengzhou Univ Zhengzhou Cent Hosp Zhengzhou Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China

Neural mass model (NMM) serves as an effective tool for understanding and exploring the complex dynamics of brain systems. Accurately estimating the model parameters of NMM is highly important for building brain models driven by observed electroencephalogram (EEG) data. However, existing methods for comparing model output with observed data primarily focus on one-dimensional linear comparisons, overlooking the highdimensional nonlinear dynamics and Riemannian geometry characteristics of EEG data. To address this issue, we propose a novel parameter estimation method for NMM based on the improved chimp optimization algorithm (ChOA) and Riemannian geometry. First, ChOA is improved by incorporating the Aquila optimizer (AOChOA) is used to improve the convergence efficiency and accuracy of the nonlinear optimization problem. Then, a novel loss function based on the Riemannian geometry of symmetric positive definite matrices (LRSPD) is constructed to capture the high-dimensional nonlinear dynamics of EEG signals. Finally, we validate the effectiveness of the proposed method by using the model output with fixed model parameters and real EEG signals as observed data, respectively. When using the model output with fixed model parameters, the loss function LRSPD yielded more accurate parameter estimation results compared to others, with the fitted model closely matching the dynamics of the observed data. When using real EEG data, the proposed method successfully recovered differences in EEG dynamics for subjects at different consciousness levels. Additionally, our study reveals the neural mechanisms of decreased consciousness level in patients with disorders of consciousness (DOC), characterized by increased inhibitory neural activity of the brain.

关键词： Neural mass model Parameter estimation chimp optimization algorithm Riemannian geometry Electroencephalogram Disorder of consciousness

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A secure routing and black hole attack detection system using coot chimp optimization algorithm-based Deep Q Network in MANET

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COMPUTERS & SECURITY 2025年 148卷

作者： Sunitha, D. Latha, P. H. Visvesvaraya Technol Univ Sambhram Inst Technol Dept Comp Sci Belagavi 590018 Karnataka India Visvesvaraya Technol Univ Sambhram Inst Technol Dept Informat Sci Belagavi 590018 Karnataka India

A Mobile Ad hoc Network (MANET) is a widely used and vibrant network, which is unevenly distributed in the environment. It is a set of self-organized independent mobile nodes interconnected without any centralized infrastructure. However, this topology nature makes the network prompt to various network security attacks. To address this issue, this paper proposes a Coot chimp optimization algorithm- Deep Q-Network (CChOA-DQN) for detecting the black hole attacks in MANET. Here, the designed CChOA is used for the identification of the optimal route in the MANET for transmitting data, which takes into fitness parameters, such as energy, distance, neighbourhood quality, link quality, and trust. The features are extracted using the Fisher score and augmented using the over-sampling technique, which is further allowed for the detection process using DQN. Also, the weights of the DQN are enhanced using the CChOA algorithmic technique to enhance the detection performance. Additionally, the results gathered from the experiment revealed that CChOA attained high performance with a maximum of 0.983 Mbps throughput, 93.70 % Packet Delivery Ratio (PDR), and minimum end-end delay of 0.096Sec, Residual energy of 0.119 J, and Control overhead of 4473.11. Also, the CChOA-DQN technique achieved the minimum False Positive Rate (FPR) of 0.122, False Negative Rate (FNR) of 0.121, Computation time of 0.153 and Run time of 0.094.

关键词： Mobile Ad hoc Network Coot algorithm chimp optimization algorithm Deep Q -Network Fisher score

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chimp optimization algorithm-Based Feature Selection for Cardiac Image-Based Heart Disease Diagnosis 1st

Chimp Optimization Algorithm-Based Feature Selection for Car...

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1st international conference Ambient Intelligence in Health Care (ICAIHC)

作者： Pradhan, Manaswini Bhuiyan, Alauddin Baliarsingh, Biren Pratap Fakir Mohan Univ Balasore Odisha India

ISBN: (纸本)9789811960703;9789811960680;9789811960673

In this paper, we propose chimp optimization algorithm (ChOA) for selection of feature to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are removed using median filter initially. Then, GLCM features are extracted from the cardiac image. Among the extracted features, optimal features are chosen using ChOA algorithm. These selected features taken as input to the classifier. In this approach, support vector neural network (SVNN) is used as classifier. The classifier classifies the image into normal and abnormal. Simulation results depict that the ChOA-based SVNN performs superior than the conventional SVNN, ANN, KNN and SVM in terms of accuracy.

关键词： Cardiac images chimp optimization algorithm GLCM feature and SVNN Heart disease diagnosis Median filter

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Application of improved chimp optimization algorithm integrating multiple strategies in reservoirs optimization operation

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Expert Systems with Applications 2025年 285卷

作者： Peiyao Yang Zhiqiang Jiang Hui Qin Yongchuan Zhang Benjun Jia School of Civil and Hydraulic Engineering Hubei Key Laboratory of Digital Valley Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science China Yangtze Power Co. Ltd. Yichang Hubei 443000 China

An improved chimp optimization algorithm integrating multiple strategies (MS-ICHOA) is developed to solve large-scale hydropower system operation optimization model. This method aims to comprehensively overcome the problem of falling into local optima easily in the later stage for the standard CHOA by incorporating different strategies into each stage of the CHOA optimization principle, that is opposition-based learning strategy based on convex lens imaging and Cauchy mutation strategy, stochastic sine convergence factor, differential mutation strategy, water wave dynamic adaptive factor and sine cosine operator, as well as Metropolis criterion. The comparison results between the proposed method and various well-known intelligent algorithms on 23 classic benchmark functions and 30 CEC2017 benchmark functions show that the MS-ICHOA exhibits superior performance regarding statistical analysis, convergence, robustness and diversity. Finally, the case study on the operation optimization of China’s Three Gorges-Gezhouba cascade reservoirs shows that the MS-ICHOA can obtain satisfactory scheduling schemes for five typical inflow scenarios, namely ultra-wet, relatively wet, normal, relatively dry and ultra-dry. Meanwhile, the average power generation generated increase by 0.16 %–4.68 %, 0.13 %–4.22 %, 0.16 %–5.80 %, 0.29 %–6.23 % and 3.19 %–4.17 % than the comparison algorithms, respectively. Therefore, MS-ICHOA provides a powerful tool to handle complex engineering optimization problems.

关键词： Cascade reservoirs chimp optimization algorithm Metaheuristic algorithm Operation optimization Power generation

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Hybrid Modified chimp optimization algorithm and Reinforcement Learning for Global Numeric optimization

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Journal of Bionic Engineering 2023年第6期20卷 2896-2915页

作者： Mohammad ShDaoud Mohammad Shehab Laith Abualigah Cuong-Le Thanh College of Engineering Al Ain University112612 Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman 11953Jordan Center for Engineering Application Technology Solutions Ho Chi Minh City Open UniversityHo Chi Minh City 700000Vietnam

chimp optimization algorithm(ChOA)is one of the most efficient recent optimization algorithms,which proved its ability to deal with different problems in various ***,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 *** 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 ***,utilizing Reinforcement Learning(RL)to improve the local research technique of OChOA,called *** way effectively avoids the algorithm falling into local *** 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 *** results and statistical experiments show that RLOChOA provides better solution quality,convergence accuracy and stability compared with other state-of-the-art algorithms.

关键词： chimp optimization algorithm Reinforcement learning Disruption operator Opposition-based learning CEC 2011 real-world problems CEC 2015 and CEC 2017 benchmark functions problems

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Recent Advances of chimp optimization algorithm:Variants and Applications

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Journal of Bionic Engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

chimp optimization algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** 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 ***,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 ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** 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 *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms chimp optimization algorithm optimization problems

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SCChOA:Hybrid Sine-Cosine chimp optimization algorithm for Feature Selection

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Computers, Materials & Continua 2023年第12期77卷 3057-3075页

作者： Shanshan Wang Quan Yuan Weiwei Tan Tengfei Yang Liang Zeng School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification ***,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search ***,the present paper proposes a hybrid Sine-Cosine chimp optimization algorithm(SCChOA)to address the feature selection *** 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 ***,an S-shaped transfer function is introduced to perform binary transformation on ***,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection *** 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 ***,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for *** results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection *** is capable of maximizing the reduction in the number of selected features while maintaining a high classification ***,the results of statistical tests also confirm the significant effectiveness of this method.

关键词： Metaheuristics chimp optimization algorithm sine-cosine algorithm feature selection and classification

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Bi-objective feature selection in high-dimensional datasets using improved binary chimp optimization algorithm

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2024年第12期15卷 6107-6148页

作者： Al-qudah, Nour Elhuda A. Abed-alguni, Bilal H. Barhoush, Malek Yarmouk Univ Dept Comp Sci Irbid Jordan Yarmouk Univ Dept Informat Technol Cybersecur Program Irbid Jordan

The machine learning process in high-dimensional datasets is far more complicated than in low-dimensional datasets. In high-dimensional datasets, Feature Selection (FS) is necessary to decrease the complexity of learning. However, FS in high-dimensional datasets is a complex process that requires the combination of several search techniques. The chimp optimization algorithm, known as ChOA, is a new meta-heuristic method inspired by the chimps' individual intellect and sexual incentive in cooperative hunting. It is basically employed in solving complex continuous optimization problems, while its binary version is frequently utilized in solving difficult binary optimization problems. Both versions of ChOA are subject to premature convergence and are incapable of effectively solving high-dimensional optimization problems. This paper proposes the Binary Improved ChOA algorithm (BICHOA) for solving the bi-objective, high-dimensional FS problems (i.e., high-dimensional FS problems that aim to maximize the classifier's accuracy and minimize the number of selected features from a dataset). BICHOA improves the performance of ChOA using four new exploration and exploitation techniques. First, it employs the opposition-based learning approach to initially create a population of diverse binary feasible solutions. Second, it incorporates the L & eacute;vy mutation function in the main probabilistic update function of ChOA to boost its searching and exploring capabilities. Third, it uses an iterative exploration technique based on an exploratory local search method called the beta-hill climbing algorithm. Finally, it employs a new binary time-varying transfer function to calculate binary feasible solutions from the continuous feasible solutions generated by the update equations of the ChOA and beta-hill climbing algorithms. BICHOA's performance was assessed and compared against six machine learning classifiers, five integer programming methods, and nine efficient popular optimiza

关键词： chimp optimization algorithm Opposition-based learning High-dimensional datasets Feature selection chimp optimization algorithm Opposition-based learning Levy flight beta-Hill climbing algorithm High-dimensional datasets Feature selection

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Variable-length CNNs evolved by digitized chimp optimization algorithm for deep learning applications

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第1期83卷 2589-2607页

作者： Khishe, Mohammad Azar, Omid Pakdel Hashemzadeh, Esmaeil Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Malek Ashtar Univ Technol Fac Naval Aviat Shiraz Iran

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.

关键词： chimp optimization algorithm Image classification Digitized-based coding ChOA

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