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Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第8期30卷 20887-20906页

作者： Soroush, Fatemeh Ehteram, Mohammad Seifi, Akram Vali E Asr Univ Rafsanjan Coll Agr Dept Water Sci & Engn POB 518 Rafsanjan Iran Semnan Univ Dept Water Engn Semnan Iran

Reliable prediction of wheat yield ahead of harvest is a critical challenge for decision-makers along the supply chain. Predicting wheat yield is a real challenge for better agriculture and food security management. Modeling wheat yield is complex and challenging, so robust tools are needed. The main aim of this study is to predict wheat yield using an advanced ensemble model. A multilayer perceptron model (MLP) was combined with optimization algorithms to determine MLP parameters as the first step in the study. Several optimization algorithms were used as optimizers, including Particle Swarm optimization (PSO), Honey Badger algorithms (HBA), Sine-Cosine algorithms (SCA), and Shark algorithms (SA). Meteorological data were inserted into models. Next, the outputs of optimized MLP models were incorporated into an inclusive multiple MLP model (IMM). A new hybrid gamma test was used to determine the most appropriate input combination. A hybrid gamma test was created by coupling the HBA with GT. This paper introduces a robust IMM model, develops an MLP model using optimization algorithms, develops a new hybrid gamma test, uses Generalized Likelihood Uncertainty Estimation (GLUE) to analyze uncertainty, and presents a spatial map of wheat yield prediction. Based on the Gamma Test analysis, mean air temperature (T-a), wind speed (WS), relative humidity (RH), evapotranspiration (ET), and precipitation (P) were the most important input parameters for reliable and accurate winter wheat yield predictions. At the testing level, the IMM model decreased the mean absolute error (MAE) of the MLP-HBA, MLP-SCA, MLP-SA, MLP-PSO, and MLP models by 47%, 52%, 55%, 58%, and 61%, respectively. In the study, the uncertainty of models based on input data was significantly lower than that of the model parameters. In addition, the GLUE analysis revealed that the wheat yield predictions were more stable and confident by considering the ensemble IMM technique. The pattern of root mean square err

关键词： Gamma test GLUE analysis meta-heuristic optimization algorithms Prediction reliability Spatial distribution

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Reconfigurable channel filtering and digital down conversion in optimal CSD space for software defined radio

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2014年第4期68卷 312-321页

作者： George, James T. Elias, Elizabeth Natl Inst Technol Dept Elect & Commun Engn Calicut 673601 Kerala India

Software defined radio (SDR) is a platform for using the same hardware to support multiple wireless communication standards. The channelizer in the SDR is used to separate the channels from the wideband signal. The features required for using the same hardware to switch between different standards are reconfigurability and low complexity. To achieve this, we use a variable bandwidth filter (VBF) with facility for the reduction and enhancement of the bandwidth, without changing the filter coefficients. This paper proposes an optimal and multiplier-less implementation of a baseband channel filter for supporting the bandwidth requirements of various wireless communication standards. This paper also discusses the concept for using it as a multi-band SDR channelizer for the direct conversion of signals from a wideband input to the baseband signal without using an intermediate stage. Frequency response masking (FRM) filter with continuous coefficients is designed and modified harmony search algorithm is used for finding the optimal canonic signed digit representation for the multiplier-less implementation. This reduces the complexity and power consumption. The VBF with the optimized FRM filter is evaluated for the selectable bandwidths starting from 1.25 to 8 MHz, which covers the bandwidth requirements of a wide range of wireless communication standards. (C) 2013 Elsevier GmbH. All rights reserved.

关键词： Variable bandwidth filter Software defined radio meta-heuristic optimization algorithms Reconfigurability Multiplier-less

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Improved slime mould algorithm by opposition-based learning and Levy flight distribution for global optimization and advances in real-world engineering problems

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第2期14卷 1163-1202页

作者： Abualigah, Laith Diabat, Ali Abd Elaziz, Mohamed Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA Zagazig Univ Fac Sci Dept Math Zagazig Egypt

Slime Mould Algorithm (SMA) is a recently introduced meta-heuristic stochastic method, which simulates the bio-oscillator of slime mould. In this paper, an improved variant of SMA is proposed, called OBLSMAL, to relieve the conventional method's main weaknesses that converge fast/slow and fall in the local optima trap when dealing with complex and high dimensional problems. Two search strategies are added to conventional SMA. Firstly, opposition-based learning (OBL) is employed to improve the convergence speed of the SMA. Secondly, the Levy flight distribution (LFD) is used to enhance the ability of the exploration and exploitation searches during the early and later stages, respectively. The integrated two search methods significantly improve the convergence behavior and the searchability of the conventional SMA. The performance of the proposed OBLSMAL method is comprehensively investigated and analyzed by using (1) twenty-three classical benchmark functions such as unimodal, multi-modal, and fixed multi-modal, (2) ten IEEE CEC2019 benchmark functions, and (3) five common engineering design problems. The experimental results demonstrate that the search strategies of SMA and its convergence behavior are significantly developed. The proposed OBLSMAL achieves promising results, and it gets better performance compared to other well-known optimization methods.

关键词： meta-heuristic optimization algorithms Slime mould algorithm Opposition-based learning Levy flight distribution Real-world optimization problems

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Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第16期32卷 12381-12401页

作者： Abualigah, Laith Amman Arab Univ Fac Comp Sci & Informat Amman Jordan

This review paper presents a comprehensive and full review of the so-called optimization algorithm, multi-verse optimizer algorithm (MOA), and reviews its main characteristics and procedures. This optimizer is a kind of the most recent powerful nature-inspired meta-heuristic algorithms, where it has been successfully implemented and utilized in several optimization problems in a variety of several fields, which are covered in this context, such as benchmark test functions, machine learning applications, engineering applications, network applications, parameters control, and other applications of MOA. This paper covers all the available publications that have been used MOA in its application, which are published in the literature including the variants of MOA such as binary, modifications, hybridizations, chaotic, and multi-objective. Followed by its applications, the assessment and evaluation, and finally the conclusions, which interested in the current works on the optimization algorithm, recommend potential future research directions.

关键词： Multi-verse optimizer algorithm meta-heuristic optimization algorithms optimization problems Nature-inspired algorithms

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Optimal Bus Layout in Transmission System by Using meta-heuristic Approaches

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2020年第12-13期48卷 1390-1400页

作者： Dogan, Erdi Yorukeren, Nuran Kocaeli Univ Dept Elect Engn Kocaeli Turkey

Transmission system expansion leads to excessive short-circuit currents that exceed the capacity of circuit breakers. To avoid over short-circuit current;transmission system operators make various alterations in the transmission system by changing the topology of the system. The feeders can be distributed to different buses in substations through disconnecting coupling circuit breaker between buses. Having been interchanged, the transmission system may face up a lot of problems even in single outage. Therefore, the optimal positions of feeders in substations are important in order to maintain system security. However, this optimization problem has non-convex properties due to AC load flow equations and it has a multi-objective structure to provide the limitation of short-circuit current and security of N-1 contingency. The constraints are the short-circuit current, voltage and transmission line limits in the single contingency. In this paper, the Genetic Algorithm and Binary Particle Swarm optimization methods were utilized to find a near-optimal bus layout. algorithms coding was made with Python programming language and PSS/E program was used to obtain power flow and short-circuit data. The results of applying the methods to the IEEE 14-bus test system demonstrated the effectiveness of the methods to take overloads away and restrict short-circuit current and hold voltage in its limit.

关键词： network topology optimization network reconfiguration bus splitting multi-objective optimization congestion management N-1 contingency analysis short-circuit current limitation meta-heuristic optimization algorithms genetic algorithm binary particle swarm optimization

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Charged System Search and Magnetic Charged System Search algorithms for Construction Site Layout Planning optimization

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2018年第4期62卷 841-850页

作者： Kaveh, Ali Khanzadi, Mostafa Moghaddam, Mohammad Rastegar Rezazadeh, Mohammad Iran Univ Sci & Technol Dept Civil Engn POB 16846-13114 Tehran Iran

Construction site layout planning can be considered as an effort to place different temporary facilities in available site locations such that multiple objectives are satisfied as much as possible. With the extension of high-rise building construction and construction activities besides the lack of available spaces in construction sites, proper utilization of this resource has been highlighted because of its significant positive influences on direct cost, safety, and security of the site which consequently affects the total cost and schedule of the project. Thus the construction site layout planning is considered as one of the essential and important phases in construction projects. Site layout planning problem is an NP-Hard problem from the viewpoint of complexity. In this research, two prominent meta-heuristic algorithms, namely Charged System Search (CSS) and Magnetic Charged System Search (MCSS) are utilized to optimize the site layout planning problem. The obtained results of implementing these two algorithms for two different types of site space modeling are compared with the results of the Particle Swarm optimization (PSO) algorithm and also those of the previous studies. The results illustrate the capability of the CSS and MCSS algorithms in solving the present problem.

关键词： construction site layout planning meta-heuristic optimization algorithms charged system search magnetic charged system search particle swarm optimization

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An automated computer-aided diagnosis system for classification of MR images using texture features and gbest-guided gravitational search algorithm

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BIOCYBERNETICS AND BIOMEDICAL ENGINEERING 2020年第2期40卷 815-835页

作者： Shanker, Ravi Bhattacharya, Mahua ABV Indian Inst Informat Technol & Management Dept ICT Gwalior 474015 Madhya Pradesh India

The segmentation and classi fication of brain magnetic resonance (MR) images are the crucial and challenging task for radiologists. The conventional methods for analyzing brain images are time-consuming and ineffective in decision-making. Thus, to overcome these limitations, this work proposes an automated and robust computer-aided diagnosis (CAD) system for accurate classi fication of normal and abnormal brain MR images. The proposed CAD system has the ability to assist the radiologists for diagnosis of brain MR images at an early stage of abnormality. Here, to improve the quality of images before their segmentation, contrast limited adaptive histogram equalization (CLAHE) is employed. The segmentation of the region of interest is obtained using the multilevel Otsu's thresholding algorithm. In addition, the proposed system selects the most signi ficant and relevant features from the texture and multiresolution features. The multiresolution features are extracted using discrete wavelet transform (DWT), stationary wavelet transform (SWT), and fast discrete curvelet transform (FDCT). Moreover, the Tamura and local binary pattern (LBP) are used to extract the texture features from the images. These features are used to classify the brain MR images using feedforward neural network (FNN) classi fier, where different meta-heuristic optimization algorithms, e.g., genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and gbest-guided gravitational search algorithm (GGGSA) are employed for optimizing the weights and biases of FNN. The extensive experimental results on DS-195, DS-180, and three standard datasets show that the classi fication accuracy of GG-GSA based FNN classi fier outperforms all mentioned meta-heuristic-based classi fiers and several state-of-the-art methods. (C) 2020 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V. All rights reserved.

关键词： Magnetic resonance imaging Multilevel Otsu's thresholding Texture and multiresolution features meta-heuristic optimization algorithms Gbest-guided gravitational search algorithm Computer-aided diagnosis system

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An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 103卷 104309-104309页

作者： Houssein, Essam H. Helmy, Bahaa El-din Rezk, Hegazy Nassef, Ahmed M. Minia Univ Fac Comp & Informat Al Minya Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj 11911 Saudi Arabia Minia Univ Elect Engn Dept Fac Engn Al Minya Egypt Tanta Univ Fac Engn Comp & Automat Control Engn Dept Tanta 31733 Egypt

meta-heuristic optimization algorithms aim to tackle real world problems through maximizing some specific criteria such as performance, profit, and quality or minimizing others such as cost, time, and error. Accordingly, this paper introduces an improved version of a well-known optimization algorithm namely Archimedes optimization algorithm (AOA). The enhanced version combines two efficient strategies namely Local escaping operator (LEO) and Orthogonal learning (OL) to introduce the (I-AOA) optimization algorithm. Moreover, the performance of the proposed I-AOA has been evaluated on the CEC'2020 test suite, and three engineering design problems. Furthermore, I-AOA is applied to determine the optimal parameters of polymer electrolyte membrane (PEM) fuel cell (FC). Two commercial types of PEM fuel cells: 250W PEMFC and BCS 500W are considered to prove the superiority of the proposed optimizer. During the optimization procedure, the seven unknown parameters (xi(1), xi(2), xi(3), xi(4), lambda, RC, and b) of PEM fuel cell are assigned to be the decision variables. Whereas the cost function that required to be in a minimum state is represented by the RMSE between the estimated cell voltage and the measured data. The obtained results by the I-AOA are compared to other well-known optimizers such as Whale optimization Algorithm (WOA), Moth-Flame optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), Particle Swarm optimization Algorithm (PSO), Harris hawks optimization (HHO), Tunicate Swarm Algorithm (TSA) and original AOA. The comparison confirmed the superiority of the suggested algorithm in identifying the optimum PEM fuel cell parameters considering various operating conditions compared to the other optimization algorithms.

关键词： Archimedes optimization algorithm (AOA) Local escaping operator meta-heuristic optimization algorithms Orthogonal learning Energy efficiency Fuel cell

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A Comparative Study of the Optimum Tuned Mass Damper for High-rise Structures Considering Soil-structure Interaction

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2021年第4期65卷 1036-1049页

作者： Kaveh, Ali Ardebili, Shaylin Rezazadeh Iran Univ Sci & Technol Sch Civil Engn Tehran *** Iran

The present paper focuses on the optimum design of tuned mass damper (TMD) as a device for control of the structures. The optimum free vibration parameters such as period and damping ratio depend on the soil condition. For this reason, the seven meta-heuristic algorithms namely colliding bodies optimization (CBO), enhanced colliding bodies optimization (ECBO), water strider algorithm (WSA), dynamic water strider algorithm (DWSA), ray optimization (RO) algorithm, teaching-learning-based optimization (TLBO) algorithm and plasma generation optimization (PGO) are used to find the TMD parameters considering soil-structure interaction (SSI) effects. These optimization methods are applied to a benchmark 40-story structure. For comparison, the obtained results of these algorithms are compared. The capability and robustness of the algorithms are investigated through the benchmark problem. The results are shown that the soil type affects the optimum values of the TMD parameters, especially for the soft soil. To evaluate the performance of the obtained parameters in both the frequency and time domains, time history displacement and acceleration transfer function of the top story of the structure are calculated for the model with and without considering the SSI effects.

关键词： meta-heuristic optimization algorithms structural control tuned mass dampers soil-structure interaction

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A hybrid optimized model for predicting evapotranspiration in early and late rice based on a categorical regression tree combination of key influencing factors

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年第1期211卷

作者： Zhao, Long Qing, Shunhao Bai, Jiayi Hao, Haohao Li, Hui Shi, Yi Xing, Xuguang Yang, Ru Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Zhumadian Co Henan Prov Tobacco Co Zhumadian 463000 Henan Peoples R China Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid Area Minist Educ Yangling 712100 Shaanxi Peoples R China China Agr Univ Coll Informat & Elect Engn Yantai 264670 Shandong Peoples R China

Crop evapotranspiration (ETC) is an important component of the agricultural hydrological cycle, and its accurate estimation is important for assessing the hydrological environment of farmlands and guiding crop irrigation. To improve the prediction accuracy of ETC for early and late rice, this study analyzed the importance of the influencing factors based on a categorical regression tree (CART) machine learning algorithm. Based on the importance results of the influencing factors obtained from CART analysis, different combinations of input factors were constructed, and daily ETC prediction models were developed for early and late rice using the least squares support vector machine (LSSVM). A three-element heuristic optimization algorithm, Sine Cosine Algorithm (SCA), Slime Mold Algorithm (SMA), and Sparrow Search Algorithm (SSA), were used to optimize the ETC model to improve the performance of the early and late rice ETC prediction models. The results of this study revealed that the strongest correlation between early and late rice ETC was net solar radiation (Rn), with importance of 0.830 and 0.802, respectively. The next strongest correlations were temperature (T), wind speed (u), soil moisture (SM) and u, leaf area index (LAI), SM (0.092, 0.02, 0.014 and 0.058, 0.049, 0.034), respectively. The accuracy of the model tended to increase as the number of influencing factors in the input combinations constructed based on the importance results increased. The accuracy of the model increased more rapidly when the input factors were one to four and the accuracy of the model increased slightly when the input factors were more than four. The SSA-LSSVM model with four-factor combinations of inputs was the least-factor combination with guaranteed model accuracy of RMSE = 0.514 mm/d, R2 = 0.924, KGE = 0.565, WI = 0.978,and MAE = 0.414 mm/d;RMSE = 0.468 mm/d, R2 = 0.910, KGE = 0.670, WI = 0.985, and MAE = 0.368 mm/d. This study may provide a reference for the prediction of eva

关键词： Rice Evapotranspiration Categorical Regression Tree Least Squares Support Vector Machine meta-heuristic optimization algorithms abbreviation Full name

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