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Developing hybrid data-intelligent method using Boruta-random forest optimizer for simulation of nitrate distribution pattern

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AGRICULTURAL WATER MANAGEMENT 2022年第0期270卷

作者： Jamei, Mehdi Maroufpoor, Saman Aminpour, Younes Karbasi, Masoud Malik, Anurag Karimi, Bakhtiar Shahid Chamran Univ Ahvaz Fac Engn Shohadaye Hoveizeh Campus Technol Dashte Azadegan Iran Univ Tehran Dept Irrigat & Reclamat Engn Tehran Iran Minist Energy Water Res Inst Dept Hydraul Hydroenvironm Engn POB 16765-313 Tehran *** Iran Univ Zanjan Water Engn Dept Fac Agr Zanjan Iran Punjab Agr Univ Reg Res Stn Bathinda Punjab India Univ Kurdistan Dept Water Sci & Engn Fac Agr Sanandaj Iran

One of the critical factors in the optimal design of drip-fertigation systems is determining the distribution of nitrate in the soil. Handling such a complex non-linear process is challenging. The main goal of this study is to develop an accurate hybrid Boruta Random Forest (BRF)-whale optimization algorithm (WOA) integrated with an Artificial Neural Network (ANN) to estimate the nitrate concentration (NO 3) in the distribution system. In addition to applying ANN and support vector regression (SVR) methods, various training algorithms and kernel functions are used as standalone validation models to evaluate the robustness of the WOA-ANN model for nitrate pattern estimation. The algorithm uses 11 variables extracted from the experimental study, which are optimally arranged in five input combinations employing the BRF Feature Selection (FS) and regression analyses. The statistical and diagnostic analyses showed that the BRF-FS is the best approach to optimize the WOA-ANN model. The proposed approach provided the best metrics (i.e. R=0.962, RMSE=0.029 mg/L, MAE=0.024, and U95%=0.056) and improved the ANN's accuracy by 30%. It also outperformed the ANN (R=0.913 and RMSE=0.042 mg/L) and SVR (R=0.901 and RMSE=0.045 mg/L) when applied to estimate the NO 3 values. An external validation analysis showed the robustness of all applied machine learning models. Moreover, the significant scoring assessment also showed that when using the BRF-FS approach, the initial nitrate concentration in soil (N0) and nitrate concentration in irrigation water (F NO 3) had the most influence on the estimation of nitrate pattern, respectively.

关键词： Artificial neural network Drip irrigation Boruta Random forest Nitrate pattern External validation whale optimization algorithm

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Layout optimisation of offshore wave energy converters using a novel multi-swarm cooperative algorithm with backtracking strategy: A case study from coasts of Australia

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ENERGY 2022年第PartE期239卷 122463-122463页

作者： Neshat, Mehdi Mirjalili, Seyedali Sergiienko, Nataliia Y. Esmaeilzadeh, Soheil Amini, Erfan Heydari, Azim Garcia, Davide Astiaso Univ Adelaide Sch Comp Sci Optimisat & Logist Grp Adelaide SA Australia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Adelaide Sch Mech Engn Adelaide SA Australia Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Sapienza Univ Rome Dept Astronaut Elect & Energy Engn DIAEE Rome Italy Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy

Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15%, 3.29%, 3.62%, 9.2%, 5.74%, and 2.43% for

关键词： Cooperative co-evolution algorithms Multi-swarm optimisation Equilibrium optimisation Multi verse optimiser Meta-heuristics Position optimisation Renewable energy Wave energy converters Particle swarm optimization whale optimization algorithm Moth-flame optimization algorithm algorithm optimization

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A Temperature Control Method of Lysozyme Fermentation Based on LRWOA-LSTM-PID

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PROCESSES 2024年第5期12卷 866页

作者： Ding, Chenhua Li, Xungen Zhou, Hanlin Yu, Jianming Du, Juling Zhao, Shixiang Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Pujiang Microelect & Intelligent Mfg Res Inst Jinhua 322200 Peoples R China China Jiliang Univ Coll Econ & Management Hangzhou 310018 Peoples R China

In order to overcome the difficulty of parameter tuning caused by the large lag and time-varying nonlinearity of the tank for lysozyme fermentation, a temperature control method based on LRWOA-LSTM-PID is proposed in this paper. Firstly, according to the intrinsic mechanism of the fermenter, a temperature mechanism model based on a dynamic equation is designed, which can better reflect the temperature changes in the fermenter. Secondly, a Proportional Integral Derivative (PID) parameter tuning method based on a Long-Short Term Memory Network (LSTM) is proposed, which takes advantage of the ability of LSTM to learn time sequence information and obtains the variation trend between error sequences under continuous time sampling, thereby adjusting network weights more reasonably and accelerating PID parameter tuning. Finally, a whale optimization algorithm (WOA) based on the L & eacute;vy flight and random walk strategy (LRWOA) is proposed for the initialization of LSTM parameters;this algorithm has excellent optimization capabilities and overcomes the problem of LSTM falling into local optimal solutions prematurely during parameter randomization. The results show that the method proposed in this paper can achieve rapid tuning of PID parameters, thereby improving the convergence speed of the system and reducing system overshoot.

关键词： PID controller fermentation process mechanism model whale optimization algorithm L & eacute vy flight strategy random walk strategy

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Short-Term Forecasting and Uncertainty Analysis of Photovoltaic Power Based on the FCM-WOA-BILSTM Model

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Cao, Wensi Zhou, Junlong Xu, Qiang Zhen, Juan Huang, Xiaobo North China Univ Water Resources & Elect Power Sch Elect Power Zhengzhou Peoples R China

Aiming to solve the problem that photovoltaic power generation is always accompanied by uncertainty and the short-term prediction accuracy of photovoltaic power (PV) is not high, this paper proposes a method for short-term photovoltaic power forecasting (PPF) and uncertainty analysis using the fuzzy-c-means (FCM), whale optimization algorithm (WOA), bi-directional long short-term memory (BILSTM), and no-parametric kernel density estimation (NPKDE). First, the principal component analysis (PCA) is used to reduce the dimensionality of the daily feature vector, and then the FCM is used to divide the weather into four categories: sunny, cloudy, rainy, and extreme weather. Second, the WOA algorithm is used to train the hyperparameters of BILSTM, and finally, the optimized hyperparameters were used to construct a WOA-BILSTM prediction model to train the four types of weather samples after FCM clustering. The NPKDE method was used to calculate the probability density distribution of PV prediction errors and confidence intervals for PPF. The RMSEs of the FCM-WOA-BILSTM model are 2.46%, 4.89%, and 1.14% for sunny, cloudy, and rainy weather types, respectively. The simulation results of the calculation example show that compared with the BP, LSTM, GRU, PSO-BILSTM, and FCM-PSO-BP models, the proposed FCM-WOA-BILSTM model has higher prediction accuracy under various weather types, which verifies the effectiveness of the method. Moreover, the NPKDE method can accurately describe the probability density distribution of forecast errors.

关键词： fuzzy c-means clustering whale optimization algorithm BiLSTM photovoltaic power forecasting uncertainty analysis

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Lifetime improvement of digital microfluidic biochips based on the IWOA

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MICROELECTRONICS RELIABILITY 2021年 123卷 114182-114182页

作者： Shi, Jinlong Fu, Ping Zheng, Wenbin Harbin Inst Technol Sch Elect & Informat Engn Harbin 150080 Peoples R China

Digital microfluidic biochips (DMFBs) are increasingly important used for clinical diagnostics drug discovery and point-of-care. Those procedures require high output precision, so the reliability and lifetime of the chips are extremely important. Due to the inherently reconfigurable nature of DMFBs, a degraded electrode may be reused many times. Therefore, the lifetime of the chips is closely related to the total actuation time of an electrode. Thus, the electrode total actuation time needs to be considered carefully in an efficient DMFB design process. This paper proposes an improved whale optimization algorithm (IWOA), which can reduce the excessive use of an electrode and reuse electrodes in an average manner to optimize the longest lifetime of DMFBs. First, the IWOA combined a WOA with a genetic algorithm, and inertial weights were added to enhance the local search ability of the IWOA. Second, the max-min WOA was proposed in the exploration phase. Lastly, the position mass of in-dividual whales was improved to represent the sequence of operations. The simulation experimental results showed that this algorithm was able to solve lifetime optimization problems. The efficiency and convergence performance of the algorithm were very good. The proposed algorithm can achieve maximum improvement in the maximum electrode activation time of 74.6%, 8.2%,15.7% and 7.2% respectively, compared with T-trees algorithm, 3-DDD algorithm, PRSA algorithm and 3D-DDMS algorithm.

关键词： Digital microfluidic chips Lifetime Protein analysis Actuation time whale optimization algorithm

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Non-destructive determination of beef freshness based on colorimetric sensor array and multivariate analysis

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SENSORS AND ACTUATORS B-CHEMICAL 2022年 369卷

作者： Xu, Weidong He, Yingchao Li, Jiaheng Deng, Yong Xu, Enbo Feng, Jinsong Ding, Tian Liu, Donghong Wang, Wenjun Zhejiang Univ Coll Biosyst Engn & Food Sci Ningbo Zhejiang Peoples R China Fuli Inst Food Sci Zhejiang R&D Ctr Food Technol & Equipment Natl Local Joint Engn Lab Intelligent Food Technol Zhejiang Key Lab Agrofood Proc Hangzhou 310058 Peoples R China Zhejiang Univ Ningbo Res Inst Ningbo 315100 Peoples R China Zhejiang Univ Coll Biosyst Engn & Food Sci 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China

Rapid and reliable detection of beef freshness is essential for diet safety and resource-saving. In this study, the colorimetric sensor array (CSA) integrated with whale optimization algorithm (WOA) and back-propagation neural network (BPNN) had been innovatively developed for the quantitative determination of beef freshness, regarding the total volatile basic nitrogen (TVB-N) and total viable counts (TVC) contents. Firstly, the CSA comprising twelve color-sensitive dyes was designed to acquire the scent fingerprints (RGB triplets) during beef storage. Secondly, WOA-BPNN was used to optimize the color components combination from the preprocessed CSA to acquire the dominant color components. Finally, the BPNN models were constructed based on the optimized color components, with the BPNN topology of 8-12-1 for TVB-N prediction and 6-6-1 for TVC prediction. The results revealed that the BPNN model combined with optimized color components could be utilized to quantitatively determine beef freshness. The overall results demonstrated that the CSA integrated with appropriate multivariable analysis methods could realize rapid and reliable quantitative determination of beef freshness. Furthermore, the WOA-BPNN could effectively extract the dominant color components, which was beneficial for improving detection accuracy and robustness of the BPNN, as well as time-and cost-saving.

关键词： Food safety Beef freshness Colorimetric sensors array whale optimization algorithm Quantitative detection

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Autonomous plume Near-Source search assisted by intermittent visible plume information using finite state Machine and YOLOv3-tiny

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Ma, Shengshan Yuan, Jie Guo, Zhenyu Wu, Qiong Xinjiang Univ Sch Elect Engn Urumqi Peoples R China

Since a plume's visual characteristics have not been fully considered and utilized, the traditional plume nearsource search methods lose a large amount of potentially available information, resulting in the low efficiency and reliability of near-source search. This paper proposes a novel plume near-source search approach assisted by the intermittent visible plume information using finite state machine and YOLOv3-tiny for a robot. A finite state machine is used to realize the autonomous behavioral decision-making of the robot according to the obtained visible plume information, olfactory information, and lidar information. In addition, the YOLOv3-tiny network is used to detect the intermittent visible plume information obtained by a depth camera to capture the color and texture of the plume. In order to improve the search reliability of the robot in the olfactory tracking behavior, a whale optimization algorithm is utilized to enable the robot to simulate the hunting behavior and the social mechanism of the whale group and conduct olfactory tracking of the plume. The experimental results show that this proposed plume near-source search approach achieves the success rates of 91.67% and 83.33% in windless and windy environments respectively, and the average total running time is 113.8 s and 159.6 s in the experimental environment with obstacles, respectively.

关键词： Intermittent Visible Plume Information YOLOv3-tiny Finite State Machine whale optimization algorithm Near-Source Search

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An efficient multi-threshold image segmentation for skin cancer using boosting whale optimizer

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年第Pt A期151卷 106227-106227页

作者： Zhu, Wei Liu, Lei Kuang, Fangjun Li, Lingzhi Xu, Suling Liang, Yingqi Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China Ningbo Univ Affiliated Hosp Med Sch Dept Dermatol Ningbo 315020 Zhejiang Peoples R China Wenzhou Med Univ Wenzhou 325035 Zhejiang Peoples R China

Due to the terrible manifestations of skin cancer, it seriously disturbs the quality of life status and health of patients, so we needs treatment plans to detect it early and avoid it causing more harm to patients. Medical disease image threshold segmentation technique can well extract the region of interest and effectively assist in disease recognition. Moreover, in multi-threshold image segmentation, the selection of the threshold set de-termines the image segmentation quality. Among the common threshold selection methods, the selection based on metaheuristic algorithm has the advantages of simplicity, easy implementation and avoidable local optimi-zation. However, different algorithms have different performances for different medical disease images. For example, the whale optimization algorithm (WOA) does not give a satisfactory performance for thresholding skin cancer images. We propose an improved WOA (LCWOA) in which the Levy operator and chaotic random mutation strategy are introduced to enhance the ability of the algorithm to jump out of the local optimum and to explore the search space. Comparing with different existing WOA variants on the CEC2014 function set, our proposed and improved algorithm improves the efficiency of the search. Experimental results show that our method outperforms the extant WOA variants in terms of optimization performances, improving the convergence accuracy and velocity. The method is also applied to solve the threshold selection in the skin cancer image segmentation problem, and LCWOA also gives excellent performance in obtaining optimal segmentation results.

关键词： Image segmentation Skin cancer whale optimization algorithm Levy operator Chaotic random mutation strategy Kapur ?s entropy

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A conformable fractional-order grey Bernoulli model with optimized parameters and its application in forecasting Chongqing's energy consumption

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Wu, Wenqing Ma, Xin Zeng, Bo Zhang, Yuanyuan Civil Aviat Flight Univ China Sch Sci Guanghan 618307 Peoples R China Southwest Univ Sci & Technol Sch Math & Phys Mianyang 621010 Peoples R China Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China

Chongqing is the only province and city with net energy input in southwest China, and its energy security has been in a tight balance for a long time. To propose scientific and reasonable energy policies for Chongqing under the "dual carbon"goal, this paper studies Chongqing's total energy consumption, coal consumption, natural gas consumption, and oil consumption by a conformable fractional -order grey Bernoulli model with optimized parameters. The new model is studied with its linearized form, the conformable fractional -order accumulation operation, the grey modelling technique, the least squares method, and the whale optimization algorithm. According to historical data, several forecasting models are built. From calculation results, the mean absolute validation percentage errors of the new model are smaller than those of other models in the total energy consumption, coal consumption and natural gas consumption, with values of 1.31%, 1.43%, and 2.41%, respectively. In the oil consumption, the MAPE valid is 3.85%, which is the second smallest among these models. The results illustrate that the new model can provide competitive results. Further, the values in the next five years are calculated.

关键词： Grey Bernoulli model Optimized parameters Linearized form whale optimization algorithm Energy consumption of Chongqing

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A hybrid model for VNF deployment capable of responding to online requests at network edge

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COMPUTER NETWORKS 2024年 247卷

作者： Zahedi, Seyed Reza Jamali, Shahram Islamic Azad Univ Dept Comp Engn Khalkhal Branch Khalkal Iran Univ Mohaghegh Ardabili Dept Comp Engn Ardebil Iran

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the whale optimization algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

关键词： Virtual network functions Edge computing Placement Real-time requests whale optimization algorithm

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