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A dual-adaptive stochastic reinforcement chimp optimization algorithm for fire detection and multidimensional problem solving

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SCIENTIFIC REPORTS 2024年第1期14卷 1-28页

作者： Zhang, Ziyang Tan, Lingye Martin, Diego Qian, Leren Khishe, Mohammad Jangir, Pradeep Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Univ Valladolid Escuela Ingn Informat Segovia Dept Informat Segovia 40005 Spain Arizona State Univ Sch Comp & Augmented Intelligence Tempe AZ 85281 USA Imam Khomeini Naval Sci Univ Nowshahr Dept Elect Engn Nowshahr Iran Yuan Ze Univ Innovat Ctr Artificial Intelligence Applicat Taoyuan Taiwan Appl Sci Private Univ Appl Sci Res Ctr Amman Jordan Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India Jadara Univ Jadara Univ Res Ctr Irbid Jordan G Era Hill Univ Dept CSE Dehra Dun 248002 India Graphic Era Deemed Be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India

chimp optimization algorithm (CHOA) is a recently developed nature-inspired technique that mimics the swarm intelligence of chimpanzee colonies. However, the original CHOA suffers from slow convergence and a tendency to reach local optima when dealing with multidimensional problems. To address these limitations, we propose TASR-CHOA, a twofold adaptive stochastic reinforced variant. The TASR-CHOA algorithm integrates two novel methodologies: a stochastic approach to improve the speed at which convergence is achieved and a dual adaptive weighting approach to optimize the exploration of early patterns, which refer to initial trends or behaviors in the algorithm's convergence process during the early stages of iterations and the exploitation of subsequent tendencies, indicating how these initial trends develop over time as the algorithm iterates and refines its search. To evaluate TASR-CHOA, we apply it to 29 conventional optimization benchmark functions, 10 IEEE CEC-06 benchmarks, 30 complicated IEEE CEC-BC benchmark functions, and ten well-known benchmark real-world challenges. We evaluate TASR-CHOA against 4 categorical optimization techniques as well as 18 top IEEE CEC-BC algorithms. Based on our broad investigation done using three statistical tests, we claim that TASR-CHOA outperforms the majority of the algorithms since within a position takes the best place, 54 out of 73 evaluation functions and engineering problems. In other cases, the results are almost the same as those of SHADE and CMA-ES over several comparisons. As an illustrative application of this joint approach, a computer-aided fire detection task is performed using a deep convolutional neural network combined with TASR-CHOA. We also outline the algorithm executed in steps, indicating computational complexity, which is O(NIxNV) + O(NIxNVx6) + O(NIxNV + 2xNIxNV) as a function of number of individuals (NI) and dimensions (NV).

关键词： chimp optimization algorithm Metaheuristic Twofold adaptive weighting IEEE CEC-BC competitions optimization Multidimensional problems

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Enhancing solar photovoltaic energy production prediction using diverse machine learning models tuned with the chimp optimization algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Al-Dahidi, Sameer Alrbai, Mohammad Alahmer, Hussein Rinchi, Bilal Alahmer, Ali German Jordanian Univ Sch Appl Tech Sci Dept Mech & Maintenance Engn Amman 11180 Jordan Univ Jordan Sch Engn Dept Mech Engn Amman 11942 Jordan Al Balqa Appl Univ Fac Artificial Intelligence Dept Automated Syst Salt 19117 Jordan Tuskegee Univ Dept Mech Engn Tuskegee AL 36088 USA

Solar photovoltaic (PV) systems, integral for sustainable energy, face challenges in forecasting due to the unpredictable nature of environmental factors influencing energy output. This study explores five distinct machine learning (ML) models which are built and compared to predict energy production based on four independent weather variables: wind speed, relative humidity, ambient temperature, and solar irradiation. The evaluated models include multiple linear regression (MLR), decision tree regression (DTR), random forest regression (RFR), support vector regression (SVR), and multi-layer perceptron (MLP). These models were hyperparameter tuned using chimp optimization algorithm (ChOA) for a performance appraisal. The models are subsequently validated on the data from a 264 kWp PV system, installed at the Applied Science University (ASU) in Amman, Jordan. Of all 5 models, MLP shows best root mean square error (RMSE), with the corresponding value of 0.503, followed by mean absolute error (MAE) of 0.397 and a coefficient of determination (R2) value of 0.99 in predicting energy from the observed environmental parameters. Finally, the process highlights the fact that fine-tuning of ML models for improved prediction accuracy in energy production domain still involves the use of advanced optimization techniques like ChOA, compared with other widely used optimization algorithms from the literature.

关键词： Solar photovoltaic Energy production Prediction Machine learning Hyperparameter tuning chimp optimization algorithm

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Social coevolution and Sine chaotic opposition learning chimp optimization algorithm for feature selection

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SCIENTIFIC REPORTS 2024年第1期14卷 1-22页

作者： Zhang, Li Chen, Xiaobo Jiangsu Univ Technol Coll Comp Engn Changzhou 213001 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Peoples Bank China Changzhou City Ctr Branch Changzhou 213001 Jiangsu Peoples R China

Feature selection is a hot problem in machine learning. Swarm intelligence algorithms play an essential role in feature selection due to their excellent optimisation ability. The chimp Optimisation algorithm (CHoA) is a new type of swarm intelligence algorithm. It has quickly won widespread attention in the academic community due to its fast convergence speed and easy implementation. However, CHoA has specific challenges in balancing local and global search, limiting its optimisation accuracy and leading to premature convergence, thus affecting the algorithm's performance on feature selection tasks. This study proposes Social coevolution and Sine chaotic opposition learning chimp optimization algorithm (SOSCHoA). SOSCHoA enhances inter-population interaction through social coevolution, improving local search. Additionally, it introduces sine chaotic opposition learning to increase population diversity and prevent local optima. Extensive experiments on 12 high-dimensional classification datasets demonstrate that SOSCHoA outperforms existing algorithms in classification accuracy, convergence, and stability. Although SOSCHoA shows advantages in handling high-dimensional datasets, there is room for future research and optimization, particularly concerning feature dimensionality reduction.

关键词： chimp optimization algorithm Feature selection High-dimensional classification data Social coevolution Sine chaotic opposition learning

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Evolving chimp optimization algorithm by weighted opposition-based technique and greedy search for multimodal engineering problems

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APPLIED SOFT COMPUTING 2023年 132卷

作者： Bo, Qiuyu Cheng, Wuqun Khishe, Mohammad 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

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.

关键词： Metaheuristics chimp optimization algorithm Opposition-based learning Greedy search

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Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartC期213卷 119206页

作者： Cai, Chengfeng Gou, Bingchen Khishe, Mohammad Mohammadi, Mokhtar Rashidi, Shima Moradpour, Reza Mirjalili, Seyedali Northwestern Polytech Univ Sch Mech Engn Xian 710072 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 Univ Human Dev Coll Sci & Technol Dept Comp Sci Sulaymaniyah Kurdistan Iraq Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the chimp optimization algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The

关键词： chimp optimization algorithm COVID-19 diagnosis Deep learning Convolutional neural networks Chest X-rays Machine learning

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Binary chimp optimization algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks

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SENSORS 2023年第8期23卷 4073页

作者： Aljebreen, Mohammed Alohali, Manal Abdullah Saeed, Muhammad Kashif Mohsen, Heba Al Duhayyim, Mesfer Abdelmageed, Amgad Atta Drar, Suhanda Abdelbagi, Sitelbanat King Saud Univ Community Coll Dept Comp Sci POB 28095 Riyadh 11437 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Dept Comp Sci Muhayil 63311 Saudi Arabia Future Univ Egypt Fac Comp & Informat Technol Dept Comp Sci New Cairo 11835 Egypt Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 16273 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Dept Comp & Self Dev Preparatory Year Deanship Al Kharj 16278 Saudi Arabia

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.

关键词： intrusion detection system wireless sensor networks machine learning chimp optimization algorithm feature selection

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Distributed power generation planning for distribution network using chimp optimization algorithm in order to reliability improvement

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期217卷

作者： Borousan, Farzaneh Hamidan, Mohammad-Ali Islamic Azad Univ IAU Dept Elect Engn Ahvaz Branch Ahvaz Iran

The most important challenges facing system designers in radial distribution networks include low voltage profiles, high current values, very high power losses and voltage drops. Therefore, the use of Distributed Gen-erations (DGs) improves the technical and economic performance of distribution systems. In this article, in order to identify the optimal location of DGs, increase voltage stability and improve reliability in 119-bus distribution network, a comparison is made of the results of chimp optimization algorithm (ChOA) and other algorithms. The objective functions implemented in ChOA include the minimization of investment and operation costs to avoid economic losses and the minimization of Energy Not-Supplied (ENS) to prevent network blackouts. The ChOA method performs better than algorithms such as the Whale optimization algorithm (WOA), the Gray Wolf optimization (GWO) algorithm, Social Spider algorithm (SSA), and Imperialist Competitive algorithm (ICA). The simulation results show that by considering DGs using ChOA in 119-bus distribution networks, the value of reliability improvement is reduced by 61.70%. The results show that the value of ENS using ChOA compared to WOA, GWO, SSA and ICA algorithms is improved by 2.60%, 4.63%, 6.37% and 9.67%, respectively. The pro-posed model shows that reliability improvement is better than other algorithms in distribution networks.

关键词： Distributed generations Radial distribution networks Reliability improvement chimp optimization algorithm

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Breast cancer diagnosis using hybrid AlexNet-ELM and chimp optimization algorithm evolved by Nelder-mead simplex approach

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第1期85卷

作者： Junyue, Cao Zeebaree, Diyar Qader Chen, Qingfeng Zebari, Dilovan Asaad Guangxi Univ Coll Life Sci & Technol Nanning 530004 Peoples R China Duhok Polytech Univ Tech Coll Informat Akre Dept Comp Network & Informat Secur Duhok Iraq Guangxi Univ Sch Comp Elect & Informat Nanning Peoples R China Nawroz Univ Coll Sci Dept Comp Sci Duhok 42001 Kurdistan Regio Iraq

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 optimization algorithms, 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.

关键词： Brest cancer diagnosis chimp optimization algorithm AlexNet Nelder-mead simplex approach

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A Novel Six-Dimensional chimp optimization algorithm-Deep Reinforcement Learning-Based optimization Scheme for Reconfigurable Intelligent Surface-Assisted Energy Harvesting in Batteryless IoT Networks

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FUTURE INTERNET 2024年第12期16卷 460-460页

作者： Shoeibi, Mehrdad Oskouei, Anita Ershadi Kaveh, Masoud Worcester Polytech Inst WPI Business Sch Worcester MA 01605 USA Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA Aalto Univ Dept Informat & Commun Engn Espoo 02150 Finland

The rapid advancement of Internet of Things (IoT) networks has revolutionized modern connectivity by integrating many low-power devices into various applications. As IoT networks expand, the demand for energy-efficient, batteryless devices becomes increasingly critical for sustainable future networks. These devices play a pivotal role in next-generation IoT applications by reducing the dependence on conventional batteries and enabling continuous operation through energy harvesting capabilities. However, several challenges hinder the widespread adoption of batteryless IoT devices, including the limited transmission range, constrained energy resources, and low spectral efficiency in IoT receivers. To address these limitations, reconfigurable intelligent surfaces (RISs) offer a promising solution by dynamically manipulating the wireless propagation environment to enhance signal strength and improve energy harvesting capabilities. In this paper, we propose a novel deep reinforcement learning (DRL) algorithm that optimizes the phase shifts of RISs to maximize the network's achievable rate while satisfying IoT devices' energy harvesting constraints. Our DRL framework leverages a novel six-dimensional chimp optimization algorithm (6DChOA) to fine-tune the hyper-parameters, ensuring efficient and adaptive learning. The proposed 6DChOA-DRL algorithm optimizes RIS phase shifts to enhance the received power of IoT devices while mitigating interference from direct and RIS-cascaded links. The simulation results demonstrate that our optimized RIS design significantly improves energy harvesting and achievable data rates under various system configurations. Compared to benchmark algorithms, our approach achieves higher gains in harvested power, an improvement in the data rate at a transmit power of 20 dBm, and a significantly lower root mean square error (RMSE) of 0.13 compared to 3.34 for standard RL and 6.91 for the DNN, indicating more precise optimization of RIS phase shifts.

关键词： Internet of Things energy harvesting reconfigurable intelligent surfaces advanced deep reinforcement learning chimp optimization algorithm

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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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