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Intrusion detection in metaverse environment internet of things systems by metaheuristics tuned two level framework

SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Antonijevic, Milos Zivkovic, Miodrag Jovicic, Milica Djuric Nikolic, Bosko Perisic, Jasmina Milovanovic, Marina Jovanovic, Luka Abdel-Salam, Mahmoud Bacanin, Nebojsa Singidunum Univ Belgrade 11000 Serbia Univ Belgrade Innovat Ctr Sch Elect Engn Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt SIMATS Saveetha Sch Engn Chennai 602105 Tamil Nadu India Sinergija Univ Bijeljina 76300 Bosnia & Herceg

Internet of Things (IoT) is one of the most important emerging technologies that supports Metaverse integrating process, by enabling smooth data transfer among physical and virtual domains. Integrating sensor devices, wearables, and smart gadgets into Metaverse environment enables IoT to deepen interactions and enhance immersion, both crucial for a completely integrated, data-driven Metaverse. Nevertheless, because IoT devices are often built with minimal hardware and are connected to the Internet, they are highly susceptible to different types of cyberattacks, presenting a significant security problem for maintaining a secure infrastructure. Conventional security techniques have difficulty countering these evolving threats, highlighting the need for adaptive solutions powered by artificial intelligence (AI). This work seeks to improve trust and security in IoT edge devices integrated in to the Metaverse. This study revolves around hybrid framework that combines convolutional neural networks (CNN) and machine learning (ML) classifying models, like categorical boosting (CatBoost) and light gradient-boosting machine (LightGBM), further optimized through metaheuristics optimizers for leveraged performance. A two-leveled architecture was designed to manage intricate data, enabling the detection and classification of attacks within IoT networks. A thorough analysis utilizing a real-world IoT network attacks dataset validates the proposed architecture's efficacy in identification of the specific variants of malevolent assaults, that is a classic multi-class classification challenge. Three experiments were executed utilizing data open to public, where the top models attained a supreme accuracy of 99.83% for multi-class classification. Additionally, explainable AI methods offered valuable supplementary insights into the model's decision-making process, supporting future data collection efforts and enhancing security of these systems.

关键词： Metaverse CatBoost LightGBM optimization Metaheuristics algorithms chimp optimization algorithm

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Environmental economic dispatch optimization using niching penalized chimp algorithm

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ENERGY 2022年第PartA期261卷

作者： Zhang, Le Khishe, Mohammad Mohammadi, Mokhtar Mohammed, Adil Hussein Shandong Acad Med Sci Shandong First Med Univ Sch Publ Hlth & Management Tai An 271016 Shandong Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Iraq Cihan Univ Erbil Fac Engn Dept Commun & Comp Engn Erbil Kurdistan Iraq

The use of traditional fossil fuels has contributed to rapid economic expansion while simultaneously having negative implications, such as increasing global warming and the devastation of the biosphere. Niching Penalized chimp optimization (NPChOA) is presented in this research in order to address the Environmental Economic Dispatch (EED) problem. In terms of global search capability, robustness, convergence rate, and durability, the proposed NPChOA outperforms the current ChOA. Following that, a novel constrained handling operator addresses the multi-objective optimization difficulty. The performance of NPChOA is evaluated using an IEEE 30 bus with six generators and a ten-unit system. The result of NPChOA is compared with Grey Wolf Optimizer (GWO), Space Reduction Strategy Particle Swarm optimization (SRSPSO), Chaotic Biogeography-Based Optimizer (CBBO), Dynamic Population Artificial Bee Colony (DPABC), Modified Bacterial Foraging algorithm (MBFA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm optimization (DE-CQPSO), standard ChOA, Dynamic Levy Flight ChOA (DLF-ChOA), and Weighted ChOA (WChOA) as the most recent modified version of ChOA to confirm its efficiency. NPChOA's evaluation score and convergence rate are outstanding compared to other benchmarks for single and multiobjective optimizations. The NPChOA's efficacy and robustness in dealing with environmental economic dispatch challenges have been demonstrated by discovering a good compromise value.

关键词： Constraint optimization Environmental Economic dispatch EED chimp optimization algorithm Niching concept

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Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm

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ENERGY 2022年第0期259卷

作者： Sun, Hongchang Niu, Yanlei Li, Chengdong Zhou, Changgeng Zhai, Wenwen Chen, Zhe Wu, Hao Niu, Lanqiang Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Urban Construct Vocat Coll Sch Architecture Jinan 250101 Peoples R China Shandong Jianzhu Univ Sch Informat & Elect Engn Shandong Key Lab Intelligent Bldg Technol Jinan 250101 Peoples R China Shandong Dawei Int Architecture Design Co LTD Inst Intelligent Bldg Jinan 250101 Peoples R China

Heating, ventilation, and air-conditioning systems provide a comfortable indoor thermal environment, but high energy consumption is often necessary to achieve an adequate level of indoor thermal comfort. However, it is challenging to design an energy-efficient thermal comfort control strategy, mainly because the internal thermal environment is influenced by complicated factors and difficult to model accurately. To solve this problem, a control strategy incorporating the parallel temporal convolutional neural network (PTCN) and the improved chimp optimization algorithm (ICHOA) is proposed for thermal comfort control of buildings. Thermal comfort control is transformed into a cost-minimization problem by establishing an objective function for both the future thermal comfort of the occupants and energy consumption and optimizing multiple air-conditioning temperature set points for the coming day. First, to ensure the prediction performance, a PTCN model was developed to predict the energy consumption and thermal comfort under different factors. An opposition -learning-based adaptive chimp algorithm was then used to solve the objective function to output the optimal set temperature. Finally, the superiority of the PTCN-ICHOA optimization strategy was verified using an office building in Jinan as an example. In winter and summer experiments, the proposed PTCN model achieved the lowest prediction errors among the models compared in terms of energy and temperature prediction. Furthermore, the PTCN-ICHOA optimization model exhibited faster convergence than the other models for both experiments. Through the proposed optimization strategy, energy consumption savings of approximately 6.3%-8.1% can be achieved while maintaining indoor thermal comfort.

关键词： Energy-saving optimization strategy Temporal convolutional neural network chimp optimization algorithm HVAC system Thermal comfort control

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ECH3OA: An Enhanced chimp-Harris Hawks optimization algorithm for copyright protection in Color Images using watermarking techniques

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KNOWLEDGE-BASED SYSTEMS 2023年第1期269卷

作者： Fahmy, Hager El-Gendy, Eman M. Mohamed, M. A. Saafan, Mahmoud M. Mansoura Univ Fac Engn Elect & Commun Engn Dept Mansoura Egypt Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura Egypt

This paper proposes a hybrid Enhanced chimp-Harris Hawks optimization algorithm (ECH3OA) that combines both enhanced chimp optimization algorithm (ChOA) and Harris Hawks optimization algorithm (HHO). HHO is a robust algorithm in solving optimization issues. However, it suffers from a balancing problem between exploitation and exploration. On the other hand, ChOA algorithm is another powerful algorithm with the capability of balancing between exploitation and exploration. However, ChOA suffers from low convergence and falling in the local minimum. The novelty of ECH3OA as a proposed optimization algorithm is concentrated in three contributions. The first is an enhanced ChOA that uses Levy Flight to improve its exploration phase. The second contribution of the algorithm is proposing an updated formula to calculate escaping energy of the prey as a selector between exploitation and exploration. Finally, the four cases of exploitation using HHO are used in the proposed algorithm. The effectiveness of the proposed algorithm is proved by comparing its performance with other eleven state-of-the-art algorithms on 23 standard benchmark function, 30 benchmark function of different modalities from CEC2017, 10 complex functions from CEC2019, and six standard real world engineering problems. Friedman average rank and Wilcoxon rank-sum (p-value) tests are applied as non-parametric statistical tests. Among the different benchmark suits, ECH3OA is ranked first among the other algorithms. We also applied ECH3OA for copyright protection in Color Images using CryptoWatermarking Techniques. The results show better durability and transparency level of the watermark as compared to recent studies.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： chimp optimization algorithm Harris Hawks optimization Benchmark functions Levy Flight Watermarking

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A novel chaotic chimp sine cosine algorithm Part-I: For solving optimization problem

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CHAOS SOLITONS & FRACTALS 2023年第1期173卷

作者： Vigya Raj, Saurav Shiva, Chandan Kumar Vedik, Basetti Mahapatra, Sheila Mukherjee, V. Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India Inst Chem Technol Mumbai Marathwada Campus Jalna India SR Univ Warangal Dept Elect & Elect Engn Warangal 506371 Telangana India Alliance Univ Bangalore 562106 India

In the realm of solving complex optimization problems, it becomes crucial to fulfill multiple objectives to achieve optimal system performance while adhering to various conditions and limitations. Traditional optimization methods often face challenges such as sluggish convergence speed and confinement to local optima, making it difficult to find satisfactory solution. To tackle this challenge, the development of multi-objective programming is directed towards satisfying physical constraints. In line of the above problems and limitations, this work aims to contribute by proposing a hybrid optimization technique that can effectively address such optimization issues. The proposed algorithm, called the chaotic chimp sine cosine (C-CHOA-SC) algorithm, combines the chimp optimization algorithm (CHOA) and the sine cosine algorithm (SCA) to overcome problems of slow convergence and getting stuck in local optima. It does this by using the SCA after the CHOA and incorporating chaos to explore and exploit the search space effectively. This hybrid approach aims to improve the algorithm's performance (offers enhanced exploration capabilities, efficient exploitation of the search space, and improved convergence speed) and find better solutions to optimization problems. These features make the C-CHOA-SC algorithm wellsuited for solving complex optimization problems with multiple objectives, providing a contemporary and effective solution. To evaluate the performance of the proposed C-CHOA-SC algorithm, three phases of analysis are conducted. In the first phase, the exploration, exploitation, and avoidance of local optima are tested using the CEC 2019 benchmark problems. The second phase involves tracking and validating the performance of C-CHOASC using multiple performance metrics on two-dimensional test functions. Finally, non-parametric alternative test i.e., the Wilcoxon rank sum test and Friedman test are performed. The results demonstrate that the proposed technique exhibits

关键词： CEC 2019 Chaotic chimp optimization algorithm Sine cosine algorithm Friedman test Wilcoxon rank-sum test

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Solar photovoltaic model parameter identification using robust niching chimp optimization

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SOLAR ENERGY 2022年 239卷 179-197页

作者： Bo, Qiuyu Cheng, Wuqun Khishe, Mohammad Mohammadi, Mokhtar Mohammed, Adil Hussein Hebei Agr Univ Inst Urban & Rural Construction Baoding Hebei Peoples R China Tsinghua Univ State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Iraq Cihan Univ Erbil Fac Engn Dept Commun & Comp Engn Erbil Iraq

Researchers are becoming increasingly interested in studying how to accurately estimate the parameters of solar PV models. In this regard, this paper proposes a newly proposed nature-inspired technique named chimp optimization algorithm (ChOA) to create accurate and dependable PV models, such as single diode, double diodes, three diodes, and PV module models. In the PV models' parameters estimation using optimization algorithms, two significant concerns need to be addressed: classifying various local/global optima and preserving these optimum values until the termination. Since ChOA is a general optimizer, it lacks an operator to address the two issues mentioned above. In order to address the mentioned problems, this paper embeds the niching technique in ChOA that includes the personal best qualities of PSO and a local search technique. In addition, a novel constraint handling approach is utilized to ensure that the algorithm is robust in tackling PV Models' parameters estimation constraints. The outcome of RN-ChOA is evaluated using seven well-known optimization algorithms, including the whippy Harris hawks optimization algorithm (WHHOA), performance-guided JAYA (PGJAYA), enriched Harris hawks optimization algorithm (EHHOA), improved JAYA (IJAYA), birds mating optimizer (BMO), flexible particle swarm optimization algorithm (FPSO), chaotic biogeography-based optimizer (CBBO), and generalized oppositional teaching-learning algorithm (GOTLA), as well as dynamic Levy flight ChOA (DLF-ChOA) and weighted ChOA (WChOA) as the most recent modified version of ChOA. Furthermore, the performance of the RN-ChOA method has been assessed in a practical application for parameter evaluation of three widely-used commercial modules, namely, multi-crystalline (KC200GT), polycrystalline (SW255), and mono-crystalline (SM55), under a variety of temperature and irradiance conditions that cause changes in the photovoltaic model's parameters. The findings demonstrate the robustness and exce

关键词： Niching concept Solar cell RN-ChOA chimp optimization algorithm Photovoltaic modules

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The optimization of nodes clustering and multi-hop routing protocol using hierarchical chimp optimization for sustainable energy efficient underwater wireless sensor networks

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WIRELESS NETWORKS 2024年第1期30卷 233-252页

作者： He, Shukun Li, Qinlin Khishe, Mohammad Salih Mohammed, Amin Mohammadi, Hassan Mohammadi, Mokhtar Chongqing Creat Vocat Coll Sch Artificial Intelligence Chongqing 402160 Peoples R China Univ Elect Sci & Technol China Fac Engn Chengdu Coll Chengdu 611731 Sichuan Peoples R China UCSI Univ Fac Engn Technol & Built Environm Dept Elect & Elect Engn Kuala Lumpur 56000 Malaysia Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Salahaddin Univ Coll Engn Dept Software & Informat Engn Erbil Kurdistan Iran Lebanese French Univ Coll Engn & Comp Sci Dept Comp Engn Erbil Kurdistan Iraq Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Iraq

The design of underwater wireless sensor networks (UWSNs) faces many challenges, including power consumption, storage, battery life, and transmission bandwidth. UWSNs usually either use node clustering or multi-hop routing as their energy-efficient optimization algorithms. The cluster optimization technique will organize the sensor nodes into a cluster network, with each cluster led by a cluster head (CH). In contrast, the multi-hop optimization algorithm will create a multi-hop network by sending data to the base station (BS) while switching between different sensor nodes. However, the overburdens of CH nodes impact the performance of the cluster optimization method, whereas the overburdens of nodes close to the BS impact the performance of the multi-hop optimization algorithm. Therefore, clustering and routing procedures can be considered as a simultaneous NP-hard problem that metaheuristic algorithms can address. With this motivation, this paper proposes an energy-efficient clustering and multi-hop routing protocol using the metaheuristic-based algorithm to increase energy efficiency in UWSNs and lengthen the network life. However, the existing metaheuristic-based methods use two separate algorithms for clustering and multi-hop routing, increasing computational complexity, different initialization, and difficulty in hyperparameters' tuning. In order to address the mentioned shortcomings, this paper proposes a novel hierarchical structure called hierarchical chimp optimization (HChOA) for both clustering and multi-hop routing processes. The proposed HChOA is validated using various metrics after being simulated using an extended set of experiments. Results are compared to those from LEACH, TEEN, MPSO, PSO, and IPSO-GWO to validate the impact of the HChOA. According to the findings, the HChOA performed better than other lifespan and energy usage benchmarks.

关键词： Nodes clustering Multi-hop routing chimp optimization algorithm Hierarchical

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Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model

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SOLAR ENERGY 2021年 222卷 1-17页

作者： Zayed, Mohamed E. Zhao, Jun Li, Wenjia Elsheikh, Ammar H. Abd Elaziz, Mohamed Yousri, Dalia Zhong, Shengyuan Mingxi, Zhu Tianjin Univ Key Lab Efficient Utilizat Low & Medium Grade Ene MOE Tianjin 300350 Peoples R China Tanta Univ Dept Power Mech Engn Fac Engn Tanta 31521 Egypt Tanta Univ Dept Prod Engn & Mech Design Tanta 31527 Egypt Zagazig Univ Dept Math Fac Sci Zagazig Egypt Fayoum Univ Dept Elect Engn Fac Engn Al Fayyum Egypt STATE GRID Int Dev Co Ltd West Changan St Beijing 100031 Peoples R China

Hybrid artificial intelligence models have become promising tools for soft computing and computational intelligence, as they can deal with complicated sustainable systems such as the prediction modeling of concentrated power systems. In these models, one or two artificial intelligence techniques are integrated with an optimization algorithm to develop a fine-tuned prediction modeling. In this paper, we develop a novel hybrid prediction model using an improved version of the Random Vector Functional Link (RVFL) network to predict the instantaneous output power and the monthly power production of a solar dish/Stirling power plant (SDSPP). A new metaheuristic algorithm called chimp optimization algorithm (CHOA) has been combined with the RVFL network to effectively determine the optimal values of RVFL parameters. More so, the proposed RVFL-CHOA model is compared with four artificial-based models include the original RVFL, and three hybrid modified versions of the RVFL model using the Particle Swarm optimization (PSO), Spherical Search optimization (SSO), and Whale optimization algorithm (WOA). The prediction performance of the five models was compared using various statistical evaluation metrics. The statistical results prove the superiority and effectiveness of the proposed RFVL-CHOA method among the other investigated optimized models for performance prediction of the SDSPP. Based on the test data, the REVL-CHOA predicts the instantaneous output power and the monthly power production of the SDSPP with determination coefficient values of 0.9992, and 0.9108, and root mean square error values of about 0.00047, and 0.05995, respectively.

关键词： Solar dish Stirling power plant Random Vector Functional Link chimp optimization algorithm Spherical search optimization Whale optimization algorithm Power production prediction

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Niching chimp optimization for constraint multimodal engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期198卷 1页

作者： Gong, Shuo-Peng Khishe, Mohammad Mohammadi, Mokhtar Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Imam Khomeini Marine Sci Univ Dept Elect Engn Nowshahr Iran Lebanese French Univ Coll Engn & Comp Sci Dept Informat Technol Erbil Kurdistan Regio Iraq

Two significant concerns need to be addressed to handle multimodal problems: classifying various local/global optima and preserving these optimum values until the termination. Besides, a comprehensive local search ability is also a need to achieve the exact global optima. chimp optimization algorithm (ChOA) is a recently swarm intelligence algorithm that needs less parameter tuning. The ChOA, on the other hand, is prone to early convergence and fails to strike a balance between exploration and exploitation when it comes to resolving multimodal challenges. In order to overcome these concerns, this paper embeds the niching technique in ChOA (NChOA) that includes the personal best qualities of PSO and a local search technique. To evaluate the NChOA's performance, we analyze it against fifteen frequently utilized multimodal numerical test functions, ten complex IEEE CEC06-2019 suit tests, and twelve constrained real-world optimization problems in a variety of engineering fields, including industrial chemical producer, process design and synthesis, mechanical design, power system, power-electronic, and livestock teed ration problems. The results indicate that the NChOA outperforms several benchmark algorithms and sixteen out of eighteen state-of-the-art algorithms by an average rank of Friedman test greater than 81% for 25 numerical functions and twelve engineering problems while outperforming jDE100 and DISHchain1e + 12 by 22% and 41%, respectively. The Bonferroni-Dunn and Holm tests demonstrated that NChOA outperforms all benchmark and state-of-the-art algorithms for all numerical functions and engineering tasks while performing comparably to jDE100 and DISHchain1e + 12. The proposed NChOA, we believe, can be used to address difficulties involving multimodal search spaces. Additionally, NChOA is more broadly applicable than rival benchmarks to a broader range of engineering applications.

关键词： Niching concept chimp optimization algorithm Constraint optimization Multimodal functions

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Ensemble Deep Learning with chimp optimization Based Medical Data Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 1643-1655页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Hamdan I.Almohammed Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Basic Medical Sciences College of Medicine.Almaarefa UniversityAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Eye state classiﬁcation acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modiﬁcations in the cognitive levels can be reﬂected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-siﬁcation model in the recognition *** 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 ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classiﬁ***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classiﬁcation results to a maximum *** 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%.

关键词： EEG eye state data classiﬁcation deep learning medical data analysis chimp optimization algorithm

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