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A Bio-Inspired Cluster optimization Schema for Efficient Routing in Vehicular Ad Hoc Networks (VANETs)

ENERGIES 2023年第3期16卷 1456页

作者： Husnain, Ghassan Anwar, Shahzad Sikander, Gulbadan Ali, Armughan Lim, Sangsoon Iqra Natl Univ Dept Comp Sci Peshawar 25100 Pakistan Univ Engn & Technol Dept Mechatron Engn Peshawar 25100 Pakistan Univ Engn & Technol Natl Ctr Artificial Intelligence NCAI Intelligent Informat Proc Lab Peshawar 25000 Pakistan COMSATS Univ Islamabad Attock Campus Islamabad 43600 Pakistan Sungkyul Univ Dept Comp Engn Anyang 14097 South Korea

Vehicular ad hoc networks (VANETs) are vital to many Intelligent Transportation System (ITS)-enabled technologies, including efficient traffic control, media applications, and encrypted financial transactions. Due to an increase in traffic, vehicular network topology is constantly changing, and sparse vehicle distribution (on highways) hinders network scalability. Thus, there is a challenge for all vehicles (in the network) to maintain a stable route, which would increase network instability. Concerning IoT-based network transportation, this study proposes a bio-inspired, cluster-based algorithm for routing, i.e., the intelligent, probability-based, and nature-inspired whale optimization algorithm (p-WOA), which produces cluster formation in vehicular communication. Various parameters, such as communication range, number of nodes, velocity, and route along the highway were considered, and their probaabilities were incorporated into the fitness function, hence resulting in randomness reduction. Results were compared to existing methods such as Ant Lion Optimizer (ALO) and Grey Wolf optimization (GWO), demonstrating that the developed p-WOA technique produces an optimal number of cluster heads (CH). The results achieved by calculating the Packet Delivery Ratio (PDR), average throughput, and latency demonstrate the superiority of the proposed method over other well-established methodologies (ALO and GWO). This study confirms statistically that VANETs employing ITS applications optimize their clusters by a factor of 75, which has the twin benefits of decreasing communication costs and routing overhead and extending the life of the cluster as a whole.

关键词： bio-inspired algorithms clustering vehicular networks whale optimization algorithm

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A whale optimization detection (WOD) method for bladder volume estimation with bioelectrical impedance spectroscopy (BIS)

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 035101-035101页

作者： Hong, Zhiyang Zhu, Jin Wu, Yang Fang, Junyang Ye, Ming Liu, Kai Yao, Jiafeng Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Soochow Univ Affiliated Hosp 2 Dept Resp & Crit Care Med Suzhou 215004 Peoples R China

A whale optimization detection (WOD) method is presented for rapid bladder volume estimation with bioelectrical impedance spectroscopy (BIS). Firstly, the WOD method for bladder volume estimation is established, which includes a bladder volume estimation electrical equivalent circuit (EEC) model and a rapid fitting strategy. Secondly, an abdominal phantom is constructed, which consists of an abdominal cavity shell and four bladders with volumes of V-b = 30 ml, 100 ml, 200 ml, and 300 ml. Next, BIS experiments are conducted using the phantom with the WOD method fitted to the obtained data. The results demonstrated a high linear correlation coefficient R-2 > 0.987 between the experimental and WOD results. Finally, in vivo testing is performed using both the Caresono HD-5 ultrasonic bladder scanner and the proposed WOD method. The EEC parameters obtained by the WOD method form a mathematical formula that accurately describes the subject's bladder volume, with a detection correlation coefficient r = 98.71%, and the maximum relative error delta = 5.88% between WOD and ultrasound testing results. In conclusion, the proposed method is expected to be applied for rapid monitoring of patients' bladder volume.

关键词： bioelectrical impedance spectroscopy electrical equivalent circuit whale optimization algorithm bladder volume estimation

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Investigating dam reservoir operation optimization using metaheuristic algorithms

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APPLIED WATER SCIENCE 2022年第12期12卷 280-280页

作者： Lai, Vivien Essam, Yusuf Huang, Yuk Feng Ahmed, Ali Najah El-Shafie, Ahmed Univ Tunku Abdul Rahman Fac Engn & Sci Dept Civil Engn Jalan Bandar Sg Long Kajang 43000 Selangor Malaysia Univ Tenaga Nasl UNITEN Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tenaga Nasl UNITEN Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

The optimization of dam reservoir operations is of the utmost importance, as operators strive to maximize revenue while minimizing expenses, risks, and deficiencies. Metaheuristics have recently been investigated extensively by researchers in the management of dam reservoirs. But the animal-concept-based metaheuristic algorithm with Levy flight integration approach has not been used at Karun-4. This paper investigates the optimization of dam reservoir operation using three unexplored metaheuristics: the whale optimization algorithm (WOA), the Levy-flight WOA (LFWOA), and the Harris hawks optimization algorithm (HHO). Utilizing a time series data set on the hydrological and climatic characteristics of the Karun-4 hydroelectric reservoir in Iran, an analysis was conducted. The objective functions and constraints of the Karun-4 hydropower reservoir were examined throughout the optimization procedure. HHO produces the best optimal value, the least-worst optimal value, the best average optimal value, and the best standard deviation (SD) with scores of 0.000026, 0.001735, 0.000520, and 0.000614, respectively, resulting in the best overall ranking mean (RM) with a score of 1.5 at Karun-4. Throughout the duration of the test, the optimized trends of water release and water storage indicate that HHO is superior to the other investigated metaheuristics. WOA has the best correlation of variation (CV) with a score of 0.090195, while LFWOA has the best convergence rate (3.208 s) and best CPU time. Overall, it can be concluded that HHO has the most desirable performance in terms of optimization. Yet, current studies indicate that both WOA and LFWOA generate positive and comparable outcomes.

关键词： Dam reservoir operation optimization Metaheuristics whale optimization algorithm Levy-flight whale optimization algorithm Harris hawk optimization

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Evaluation of bio-inspired optimization algorithms hybrid with artificial neural network for reference crop evapotranspiration estimation

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 190卷 106466-106466页

作者： Gao, Lili Gong, Daozhi Cui, Ningbo Lv, Min Feng, Yu Chinese Acad Agr Sci Inst Environm & Sustainable Dev Agr State Engn Lab Efficient Water Use Crops & Disast Beijing Peoples R China Sichuan Univ State Key Lab Hydraul & Mt River Engn Chengdu Peoples R China Sichuan Univ Coll Water Resource & Hydropower Chengdu Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Yangling Shaanxi Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn Shenzhen Peoples R China

Reference crop evapotranspiration (ETo) is a determinant factor in agricultural water resource management. Therefore, accurate ETo information is critical to quantify crop water requirements for precision agriculture management. This study coupled bio-inspired optimization algorithms with artificial neural network (ANN), i.e., ANN with bat algorithm (BA-ANN), ANN with cuckoo search algorithm (CSA-ANN), and ANN with whale optimization algorithm (WOA-ANN), and developed three hybrid ANN models for daily ETo modeling with limited inputs. The models were trained and evaluated using a k-fold test approach and long-term daily climatic data from 2001 to 2018 at six climatic stations in the Loess Plateau of north China. Three input scenarios were used, including temperature-based inputs, radiation-based inputs, and mass transfer-based inputs. The statistical comparison showed that the hybrid WOA-ANN offered better estimates than BA-ANN and CSA-ANN in all three input scenarios. In general, the radiation-based WOA-ANN provided the most accurate ETo estimations, with regional average relative root mean square error and Nash-Sutcliffe efficiency coefficient of 13.3% and 0.959, respectively. The temperature-based WOA-ANN offered acceptable and reasonable ETo estimates. Thus, it is a reliable tool for ETo modeling, given that air temperature is available in many regions. Overall, the bio-inspired optimization algorithms are robust tools for enhancing ANN performance in ETo simulation, and thus they are highly recommended to estimate ETo in the study region. Our study proposed powerful models for accurately estimating ETo with limited inputs, offering practical implications for the development of precision agriculture.

关键词： Machine learning Evapotranspiration Agricultural water resources Precision agriculture whale optimization algorithm

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optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 97卷 104015-104015页

作者： Zhou, Jian Qiu, Yingui Zhu, Shuangli Armaghani, Danial Jahed Li, Chuanqi Hoang Nguyen Yagiz, Saffet Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hanoi Univ Min & Geol Min Fac Dept Surface Min 18 Vien St Hanoi Vietnam Hanoi Univ Min & Geol Ctr Min Electromech Res 18 Vien St Hanoi Vietnam Nazarbayev Univ Sch Min & Geosci Nur Sultan City 010000 Kazakhstan

The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key parameter for the successful accomplishment of a tunneling project, and the proper and reliable prediction of this parameter can lead to minimizing the risks associated to high capital costs and scheduling for such projects. This research aims at optimizing the hyper-parameters of the support vector machine (SVM) technique through the use of three optimization algorithms, namely, gray wolf optimization (GWO), whale optimization algorithm (WOA) and moth flame optimization (MFO), in forecasting TBM AR. In fact, the role of these optimization techniques is to optimize the hyperparameters 'C' and 'gamma' of the SVM model to get higher performance prediction. To develop the hybrid SVM-based models, 1,286 sample sets of data collected from a water transfer tunnel in Malaysia comprising seven input variables, i.e., rock mass rating, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force and revolution per minute, and one output variable, i.e., TBM AR, were considered and used. Several GWO-SVM, WOA-SVM and MFO-SVM models were constructed to predict TBM AR considering their effective parameters. The accuracy levels of the proposed models were assessed using four statistical indices, i.e., the coefficient of determination (R-2), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models. R-2 of (0.9623 and 0.9724), RMSE of (0.1269 and 0.1155), and VAF of (96.24 and 97.34%), respectively, for training and test stages of the MFO-SVM model confirmed that this hybrid SVM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.

关键词： TBM performance Support vector machine whale optimization algorithm Gray wolf optimization Moth flame optimization

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Dynamic Reactive Power optimization Strategy for AC/DC Hybrid Power Grid Considering Different Wind Power Penetration Levels

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IEEE ACCESS 2024年 12卷 187471-187482页

作者： Feng, Nan Feng, Yuyao Su, Yun Zhang, Yajun Niu, Tao State Grid Shanghai Elect Power Co Ltd Elect Power Res Inst Shanghai 200437 Peoples R China Chongqing Univ Elect Engn Dept Chongqing 400044 Peoples R China

With the increasing capacity of wind power integration and the further development of AC/DC hybrid power grids, the problem of voltage stability in the receiving end has become increasingly severe. The installation of dynamic reactive power compensation devices is a feasible measure to improve the voltage stability of the receiving end power grid. This paper proposes a dynamic reactive power optimization strategy for AC/DC hybrid power grids considering the integration of new energy sources. Firstly, a detailed mathematical model of the AC/DC hybrid system is established. In order to enhance the voltage restoration capability after a fault, the definition of a fault recovery indicator is provided. Subsequently, an optimal scheduling strategy for dynamic reactive power compensation devices is formulated to maximize the value of the fault recovery indicator. Considering the nonlinearity and non-convexity of the optimization model, trajectory sensitivity method and whale optimization algorithm are adopted to enhance the solution efficiency. Finally, an improved IEEE-9 test system is employed to demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Reactive power Power system dynamics Mathematical models Transient analysis Power system stability Steady-state Power grids AC-DC power converters Wind power generation whale optimization algorithms Fault detection AC/DC hybrid power grids wind power dynamic reactive power fault recovery indicator trajectory sensitivity method whale optimization algorithm

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An Efficient Geometric Parameter optimization Method for Microstrip Bandpass Filter Based on One-Dimensional Convolutional Neural Network

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2024年第1期2024卷

作者： Gao, Yang Wang, Danyang Yu, Huilong Hua, Tao Hou, Ning Lu, Yapeng China Three Gorges Univ Dept Elect Engn & New Energy Yichang 443002 Peoples R China Henan Univ Urban Construct Sch Elect & Control Engn Pingdingshan 467000 Peoples R China Nanjing Inst Technol Sch Informat & Commun Engn Nanjing 211167 Peoples R China

Machine learning-assisted electromagnetic simulation has become an effective acceleration tool for designing microwave components by introducing high-precision models and optimization algorithms, featuring fast design and high efficiency. However, enormous amount of data generated from the blind preliminary and computationally expensive simulation is required to predict the accuracy response. An efficient geometric parameter optimization method for microstrip bandpass filter (BPF) based on a one-dimensional convolutional neural network is proposed. Nonlinear convergence factor, adaptive weight, and Gaussian difference mutation strategies are integrated using the whale optimization algorithm to avoid the local optimum and improve optimization accuracy. Computational efficiency is improved significantly with small-scale training data. The validity and efficiency of the proposed method are confirmed by fifth-order microstrip BPFs, and the performance of the predicted structure parameters is significantly improved, which shows great promise for application in optimization and performance improvement in microwave electromagnetic simulation.

关键词： microstrip bandpass filter one-dimensional convolutional neural network whale optimization algorithm

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whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第0期83卷 000-000页

作者： Zhang, Meilin Wu, Qianxi Chen, Huiling Heidari, Ali Asghar Cai, Zhennao Li, Jiaren Abdelrahim, Elsaid Md. Mansour, Romany F. Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Wenzhou Peoples Hosp Wenzhou 325099 Zhejiang Peoples R China Northern Border Univ Fac Sci Ar Ar Saudi Arabia Tanta Univ Fac Sci Tanta Egypt New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which com-bines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed al-gorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved opti-mizer with efficient value.

关键词： whale optimization algorithm Random contraction strategy Rosenbrock method Swarm intelligence Feature selection COVID-19 WOA

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Multi-sensor image fusion based on contrast and directional features optimization

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2018年第12期14卷

作者： Jin, Haiyan Zhang, Meng Xiao, Zhaolin Li, Yaning Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Shaanxi Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian Shaanxi Peoples R China

Multi-sensor image fusion is always an important and opening problem, which can enhance visual quality and benefit some social security applications. In this article, we use contrast pyramid to decompose visible and infrared images, respectively, and the directional filter banks are applied to obtain multiple directional sub-band image features. Then, we compute the decomposition coefficients of visible and infrared images using a low-pass filter on the decomposed data;and finally, we introduce the whale optimization algorithm to search optimal coefficients to reconstruct the final fusion image. The experiments are conducted on multiple datasets with subjective and objective comparisons, in which the qualitative and quantitative analyses indicate the validity of the proposed method.

关键词： Image fusion multi-sensors directional features optimization whale optimization algorithm

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A Novel Hybrid Genetic-whale optimization Model for Ontology Learning from Arabic Text

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algorithmS 2019年第9期12卷 182-182页

作者： Ghoniem, Rania M. Alhelwa, Nawal Shaalan, Khaled Mansoura Univ Dept Comp Mansoura 35516 Egypt Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 84428 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Arts Dept Arabic Riyadh 84428 Saudi Arabia British Univ Dubai Fac Engn & IT Dubai 345015 U Arab Emirates

Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language's nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept's path distance, (2) the concept's distribution layer, and (3) the mutual parent concept's distribution layer. Then, feature mapping is performed by assigning the concepts' similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA's mutation, crossover, and selection processes with the WOA's processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approa

关键词： text mining ontology learning hybrid models genetic algorithms whale optimization algorithm

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