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artificial hummingbird-Based Optimisation with Advanced Crowding Distance of Energy Reduction in the Polyethylene Reactors

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2024年第1期8卷 271-284页

作者： Rohman, Fakhrony Sholahudin Alwi, Sharifah Rafidah Wan Muhammad, Dinie Idris, Iylia Zahan, Khairul Azly Murat, Muhamad Nazri Azmi, Ashraf Univ Teknol Malaysia Res Inst Sustainable Environm RISE Proc Syst Engn Ctr UTM PROSPECT Johor Baharu 81310 Malaysia Univ Teknol Malaysia UTM Fac Engn Sch Chem & Energy Engn Johor Baharu 81310 Johor Malaysia Univ Teknol MARA Coll Engn Sch Chem Engn Shah Alam 40450 Selangor Malaysia Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan Univ Tun Hussein Onn Malaysia Fac Engn Technol Batu Pahat 86400 Johor Malaysia Univ Sains Malaysia Sch Chem Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia

Ethylene is polymerised by free radicals under extreme conditions of high pressure and temperature to produce low-density polyethylene LDPE. Considering the requirement for high compression power and heating-cooling elements, combined with depleting fossil fuel and climate change issues, an approach is needed to trade-off these issues. As such, an effective approach of multi-objective optimisation study to obtain the optimum production of the LDPE with minimum energy consumption is proposed in this work. The multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance (MOAHA-DECD) executes within ASPEN Plus-MATLAB environment for energy saving of low-density polyethylene (LDPE) production. Three problems are addressed: minimise energy cost and maximise productivity for problem 1 (P1);minimising energy cost and maximising conversion for problem 2 (P2);and minimising energy cost, maximising productivity, and maximising conversion for problem 3 (P3). The inlet pressure, the mass flow rate of Initiator 1 (tert-butyl peroxypivalate, TBPPI), and the mass flow rate of Initiator 2 (tert-butyl 3,5,5trimethyl-peroxyhexaonate (TBPIN)) of the reacting zones (zone 3 and zone 5) are considered as decision variables. Pareto solutions obtained are arrayed across the entire Pareto front (PF) with an even sweep and diverse points. Based on the results, the highest productivity, lowest energy cost, and highest conversion are 554.958 Mil. RM/year, 61.388 Mil. RM/year, and 0.320. The decision variable plots show that the mass flow rate of the initiator at the end zone of the reactor highly impacts the optimal option. For the next study, the generated Pareto allows decision-makers to select the most acceptable solution based on their preferences to trade-off economic, energy, and environmental issues.

关键词： Low-density polyethylene Tubular reactor Multi-objective optimisation artificial hummingbird algorithm Pareto front

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Admixed high-performance concrete property prediction by novel regression-based models

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期45卷 4209-4221页

作者： Cai, Huiwang Luan, Ji Zhou, Changlin Zhang, Ji Ma, Lu Wuhan Univ Dept Logist Support Wuhan Hubei Peoples R China Gen Inst Architectural Design & Res Co Ltd Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Hubei Peoples R China Hubei Weixi Architectural Design Consulting Co LT Wuhan Hubei Peoples R China Natl Ctr Technol Innovat Digital Construct Wuhan Hubei Peoples R China

High-performance concrete (HPC) is one of the most important elements in constructing bridges, skyscrapers, and dams. This concrete additive plays a very important role in performance and response to inflow loads such as earthquakes and dead loads. Fly ash (Fa) and Micro-silica (Ms) are additives added to concrete by cement to reduce water to cement. Increase the ratio and increase the hardening of the cement. This will improve the compressive strength (Cs) of the concrete. Modeling is required for this type of structure. The radial basis function (RBF) is one of the models that can produce better and more rational results. This model combines two optimizers, the Sine Cosine algorithm (SCA) and the artificial hummingbird algorithm (AHA), in the framework of RBF-SCA and RBF-AHA, which are considered to be new and effective initiatives in the field of algorithms. The lowest amount of error parameters contains: (RMSE = 2.58), (NMSE = 6.59), and (U95 = 7.16) for RBF-AHA in the train section and the test section (MBE = -0.1929). The (Tstate = 0.285) in the train section of the RBF-SCA has the lowest compared to another section. RBF-AHA has the highest R2 value of 97.15% in the training area. Both hybrid models can have the desired error and the correct percentage based on the given output. However, the RBF-AHA model may look more powerful in this modeling.

关键词： High-performance concrete compressive strength radial basis function artificial hummingbird algorithm sine cosine algorithm

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Parameter Extraction of Photovoltaic Cells and Modules by INFO algorithm

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IEEE ACCESS 2022年 10卷 87022-87052页

作者： Demirtas, Mehmet Koc, Kezban Gazi Univ Fac Technol Dept Elect & Elect Engn TR-06560 Ankara Turkey

This study presents the extraction of unknown parameters of various photovoltaic (PV) cells and modules by using the weighted mean of vectors (INFO) algorithm. The parameter estimation of PV cells and modules is one of the most important issues in the design of effective PV power systems. Since the PV parameters are highly nonlinear and complex in nature, the estimation of these parameters also becomes a challenging optimization problem for designers. The main challenge is to obtain the most accurate estimation. In order to solve the problem in a unique way, the state-of-the-art metaheuristic algorithms that have not been tried so far in parameter extraction are chosen. The selected ones are the INFO optimization algorithm, the artificial hummingbird algorithm (AHA), the artificial ecosystem-based optimization (AEO) algorithm, the runge kutta (RUN) optimizer, and lastly the reptile search algorithm (RSA). The motivation here is to test as many algorithms as possible to reach to the most accurate solution. In addition, the gray wolf optimizer (GWO), the frequently used one in literature due to its superiority in parameter extraction applications, is selected to validate the results of the evaluated algorithms through comparison against the GWO. Moreover, the performances of these algorithms are compared with evaluation metrics consisting of minimum, mean, maximum, standard deviation, and statistical tests using Wilcoxon signed-rank test and Friedman test. At the end of the study, it is demonstrated that the INFO, statistically, produces the highest accuracy and reliable results. Due to its statistical success compared to other algorithms, the INFO is used to extract the parameters of a commercially available PV cells and modules. It is clearly shown that the parameters extracted by the INFO closely match the parameters provided by the manufacturer's datasheet, which points out the superiority of the INFO algorithm in PV modeling.

关键词： Optimization Integrated circuit modeling Adaptation models Computational complexity Sociology Parameter extraction Resistance Ecosystems artificial ecosystem-based optimization algorithm artificial hummingbird algorithm diode model Friedman test grey wolf optimizer parameter extraction reptile search algorithm runge kutta optimizer weighted mean of vectors Wilcoxon signed rank test

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Design of Optimal FOPI Controller for Two-Area Time-Delayed Load Frequency Control System with Demand Response

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第7期19卷 4073-4085页

作者： Katipoglu, Deniz Soylu, Selim Aksaray Univ Dept Elect & Elect Engn Aksaray Turkiye

This study comprehensively investigates modeling and controller design for two-area load frequency control (LFC) system with demand response (DR) and time delays. An optimized fractional order proportional-integral (FOPI) controller is designed both to improve the stability of grid frequencies and to deal with the time delays. Using the stability boundary locus (SBL) method, the stability region for the FOPI controller parameter space is obtained. The boundaries of these stability regions are used in the optimization process of the FOPI controller parameters. A new objective function is used to minimize the integral time-weighted absolute error (ITAE) and time domain specifications such as overshoot (OS) and undershoot (US) of the frequency and tie-line power deviations. The parameters of the FOPI controller are optimized by artificial hummingbird algorithm (AHA). The simulation results are compared with gray wolf optimizer (GWO) and particle swarm optimization (PSO) tuned FOPI controllers. The AHA tuned FOPI (AHA-FOPI) controller gives very promising results, especially in terms of improving the transient response of the system by reducing the settling time (ST) and steady-state error (ess). Additionally, the effectiveness of the AHA-FOPI is demonstrated under increased time delay. It is evident from the simulation results that the AHA-FOPI controller has successfully improved the time domain characteristics of the LFC-DR system.

关键词： Load frequency control Demand response Stability region Fractional order PI artificial hummingbird algorithm Transient response

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Hybrid optimized MRF based lung lobe segmentation and lung cancer classification using Shufflenet

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第17期83卷 52335页

作者： Spoorthi, S. Mahesh, Shanthi Visvesvaraya Technol Univ Comp Sci & Engn Atria Inst Technol Bengaluru India VTU ISE Dept Atria Inst Technol Bengaluru India

Lung cancer is a kind of harmful cancer type that originates from the lungs. In this research, the lung lobe segmentation is carried out using Markov Random Field (MRF)-based artificial hummingbird Cuckoo algorithm (AHCA). The AHCA algorithm is modelled by considering the benefits of both the artificial hummingbird algorithm (AHA) and the Cuckoo search (CS) algorithm. Moreover, the lung cancer classification is done with ShuffleNet, which is trained by the artificial hummingbird Firefly optimization algorithm (AHFO) which is the integration of AHA and Firefly algorithm (FA). In this research, two algorithms are devised for both segmentation and classification. From these two algorithms, the AHA algorithm is used for updating the location. The AHA algorithm had three phases, such as foraging, guided foraging and migrating foraging where the guided foraging stage is selected to update the location for both segmentation and classification. Besides, the developed AHFO-based ShuffleNet scheme attained superior performance with respect to the testing accuracy of 0.9071, sensitivity of 0.9137 and specificity of 0.9039. The performance improvement of the proposed method for testing accuracy is 6.615%, 3.197%, 2.756%, and 1.764% higher than the existing methods. In future, the performance will be boosted by the advanced scheme for identifying the grade of disease.

关键词： Firefly algorithm artificial hummingbird algorithm Cuckoo search algorithm Markov random field ShuffleNet scheme

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Strength property estimation of high-performance concrete with novel admixtures using optimal regression-based approaches

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第4期45卷 6655-6666页

作者： Wang, Libo Zhao, Jun Guo, Shizhong Anyang Inst Technol Sch Civil & Architectural Engn Anyang Henan Peoples R China Hebei Univ Technol Tangshan Hebei Peoples R China

Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R-2), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE= 2.00 (MPa), R-2 = 98.59%, MAE= 0.717 (MPa), MAPE= 1.22 (MPa), and WAPE = 0.114 (MPa).

关键词： High-performance concrete sine cosine algorithm artificial hummingbird algorithm support vector regression compressive strength

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Hybrid Optimized Deep Learning-Based Bacilli Segmentation and Infection-Level Identification of Tuberculosis Using Sputum Images

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND artificial INTELLIGENCE 2024年第1期38卷 2357017-2357017页

作者： Sathish, P. Preethi, D. Dominic, Clara Shanthi Kadiravan, G. NITTE Meenakshi Inst Technol Dept Master Comp Applicat Bangalore 560064 Karnataka India JAIN Deemed be Univ Dept Comp Sci & IT Bengaluru 560078 Karnataka India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

Presently, one of the foremost health issues and an extremely transferrable disease is Tuberculosis which is spreading worldwide. Tuberculosis is generally produced by mycobacterium tuberculosis and can cause death if it is not detected at premature stages. Therefore, a precise and efficient approach is essential for the identification of tuberculosis. The physical analysis of sputum smears through fluorescence microscopy for the detection process and also the treatment process is a subjective and difficult task. Thus, the hybrid optimization-driven deep learning approach is designed in this paper for infection-level identification of severity prediction of tuberculosis. Here, a sputum image is considered for performing tuberculosis detection and it is acquired from a database. For executing bacilli segmentation, the Deep Contour Aware Network (DCAN) model is used. In addition, DCAN is trained by the introduced Fractional artificial hummingbird algorithm (FAHA). Additionally, the LeNet scheme is applied to detect the infection level and severity detection. The LeNet is also trained using Coot-FAHA thereby;the system performance is highly increased. The devised optimization-enabled deep learning method achieved improved performance with precision, F1-score, and recall of 0.9326, 0.9386, and 0.9163, respectively.

关键词： Deep contour aware network pyramid histogram of oriented gradients artificial hummingbird algorithm fractional calculus

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IoT-based intelligent waste management system

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NEURAL COMPUTING & APPLICATIONS 2023年第32期35卷 23551-23579页

作者： Ahmed, Mohammed M. Hassanien, Ehab Hassanien, Aboul Ella Univ Sadat City Fac Comp & Artificial Intelligence Sadat City Egypt Cairo Univ Fac Comp & Artificial Intelligence Giza Egypt Sci Res Grp Egypt SRGE Cairo Egypt

Recently, the population density in cities has increased at a higher pace, so waste generation is on the rise in most societies due to population growth. Given this concern, it would be highly important to manage waste generation. Intelligent city planning is necessary to improve the quality of city life and make cities more livable. This paper presents an intelligent waste management system (IWMS) in smart cities based on Internet of Things components like sensors, detectors, and actuators. IWMS contains three main phases. The first phase of the system is to adapt the low energy adaptive clustering hierarchy approach as an optimization process to better balance the energy consumption of smart waste bins (SBs), thus leading to extending the life of the smart waste network. The second phase is handling the missing values which are retrieved from SBs using an improved version of the k-nearest neighbor algorithm based on artificial hummingbird optimization (AHA), while the third phase presents an optimal energy-efficient route process for the routing of waste trucks that improves fuel efficiency and reduces the time to get an appropriate SB. According to the experimental results, the proposed system has achieved energy savings of 34% for the smart waste bin network. Moreover, compared to other systems, it has a lower mean error rate when generating missing values, and the results related to convergence and running time validate its superiority compared with other metaheuristic algorithms.

关键词： Waste management IoT Missing data Optimization artificial hummingbird algorithm

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FINE-TUNING MobileNetV3 WITH DIFFERENT WEIGHT OPTIMIZATION algorithmS FOR CLASSIFICATION OF DENOISED BLOOD CELL IMAGES USING CONVOLUTIONAL NEURAL NETWORK

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2024年第6期22卷 65-93页

作者： Dhas, M. Mohana Singh, N. Suresh Manonmaniam Sundaranar Univ Annai Velankanni Coll Dept Comp Sci Tirunelveli 627012 Tamil Nadu India Malankara Catholic Coll Dept Comp Applicat Mariagri Kaliyakkavilai India

Breast cancer remains a formidable global health concern, underscoring the urgency for advanced diagnostic methodologies. This research presents a multifaceted framework aimed at significantly enhancing breast cancer diagnosis through innovative approaches in image processing and machine learning. The proposed framework encompasses several key contributions. Firstly, a robust denoising strategy is implemented using Convolutional Neural Network encoderdecoder architecture, augmented with data augmentation techniques. This addresses the challenge of vanishing gradients through enhanced Rectified Linear Units based Convolutional Neural Network, enhancing the model's generalization capability. Subsequent to denoising, feature extraction is performed utilizing a fine-tuned MobileNetV3 model. The model's performance is optimized through Modified Rectified Linear Units and NRMSProp approaches, effectively eliminating undesired features and improving overall efficiency. Crucially, a novel feature selection process is introduced, leveraging the artificial hummingbird algorithm based on Manta Ray Foraging Optimization algorithm. This algorithm selectively identifies essential features from breast cancer images, significantly elevating classification accuracy. To validate the proposed framework, a comprehensive evaluation is conducted, comparing its performance with a hybrid of five different metaheuristic algorithms, including Marine Predators algorithm, Tunicate Swarm algorithm, Manta Ray Foraging Optimization algorithm, Arithmetic Optimization algorithm, and Jelly Fish optimization algorithm. artificial hummingbird algorithm based on Manta Ray Foraging Optimization algorithm emerges as the most effective among these algorithms, showcasing superior performance. The evaluation utilized the Breast Cancer Histopathological Image Classification dataset, resulting in an impressive classification accuracy of 99.51% for the proposed model.

关键词： convolutional neural network artificial hummingbird algorithm marine predators algorithm MobileNetV3 improved ReLU

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Black-box modeling of ship maneuvering motion using multi-output least-squares support vector regression based on optimal mixed kernel function

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OCEAN ENGINEERING 2024年 293卷

作者： Jiang, Lichao Shang, Xiaobing Jin, Bao Zhang, Zhi Zhang, Wen Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Shanghai Inst Aerosp Syst Engn Shanghai Peoples R China

Black-box modeling has been widely used to predict ship maneuvering motion. A novel black-box modeling approach using optimal mixed kernel multi-output least-squares support vector regression (MMLSSVR) based on the artificial hummingbird algorithm (AHA) is proposed in this paper. To improve the performance of the MMLSSVR model, a mixed kernel function that combines a radial basis function and a polynomial kernel function to capture both global and local properties is proposed. The optimal hyper-parameters of the mixed kernel function are determined by the AHA, which employs the linear combination of the mean square error and the determinant of error covariance as the objective function to balance between prediction accuracy and stability. The performance of the proposed method is evaluated using SR108 large container ship data. The experimental results demonstrate that the proposed MMLSSVR model achieves desirable prediction accuracy and strong generalizability for ship maneuvering motion modeling.

关键词： Black modeling Kernel function System identification artificial hummingbird algorithm Ship maneuvering motion

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