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Maximum power point tracking in partially shaded photovoltaic systems using grasshopper optimization algorithm

IET RENEWABLE POWER GENERATION 2023年第2期17卷 389-399页

作者： Tabar, Sadegh Mahmoodi Shahnazari, Mostafa Heshmati, Kamila Yazd Univ Elect Engn Dept Yazd *** Iran

The P-V curve of photovoltaic (PV) arrays has several peaks under partial shading conditions (PSCs). Therefore, to extract maximum power, it is necessary to detect its global maximum power point (GMPP). The conventional maximum power point tracking (MPPT) algorithms are trapped in local maximum power point (LMPP) under PSCs. In this paper, a MPPT method based on grasshopper optimization algorithm (GOA) has been presented. A PV system has been implemented in Matlab/Simulink and different operating conditions have been investigated to compare the performance of the proposed method with two well-known grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms. Furthermore, an experimental setup has been developed to verify the efficiency of the proposed MPPT method. The simulation and experimental results confirmed the speed and accuracy of convergence compared to GWO and PSO algorithms.

关键词： Matlab partial shading conditions Optimisation techniques Solar power stations and photovoltaic power systems DC-DC power convertors local maximum power point conventional maximum power point tracking algorithms maximum power point trackers Solar cells and arrays particle swarm optimisation global maximum power point PV system partially shaded photovoltaic systems solar cell arrays photovoltaic arrays power generation control Control of electric power systems Power electronics, supply and supervisory circuits grasshopper optimization algorithm Power engineering computing power engineering computing photovoltaic power systems optimisation MPPT method

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A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2018年 108卷 58-72页

作者： Zhang, Xin Miao, Qiang Zhang, Heng Wang, Lei Sichuan Univ Sch Aeronaut & Astronaut Chengdu 610065 Sichuan Peoples R China

The mode number and mode frequency bandwidth control parameter (or quadratic penalty term) have significant effects on the decomposition results of the variational mode decomposition (VMD) method. In the conventional VMD method, the values of decomposition parameters are given in advance, which makes it difficult to achieve satisfactory analysis results. To address this issue, this paper proposes a parameter-adaptive VMD method based on grasshopper optimization algorithm (GOA) to analyze vibration signals from rotating machinery. In this method, the optimal mode number and mode frequency bandwidth control parameter that match with the analyzed vibration signal can be estimated adaptively. Firstly, a measurement index termed weighted kurtosis index is constructed by using kurtosis index and correlation coefficient. Then, the VMD parameters are optimized by the GOA algorithm using the maximum weighted kurtosis index as optimization objective. Finally, fault features can be extracted by analyzing the sensitive mode with maximum weighted kurtosis index. Two case studies demonstrate that the proposed method is effective to analyze machinery vibration signal for fault diagnosis. Moreover, comparisons with the conventional fixed-parameter VMD method and the well-known fast kurtogram method highlight the advantages of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Vibration signal analysis Fault diagnosis Variational mode decomposition grasshopper optimization algorithm Weighted kurtosis index Parameter adaptive estimation

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Multiple Filter-Based Rankers to Guide Hybrid grasshopper optimization algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

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IEEE ACCESS 2021年 9卷 74127-74142页

作者： Sharifai, Abdulrauf Garba Zainol, Zurinahni Binti Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Malaysia Yusuf Maitama Sule Univ Dept Comp Sci Kano 700252 Nigeria

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.

关键词： optimization Feature extraction Filtering algorithms Classification algorithms Search problems Task analysis Simulated annealing grasshopper optimization algorithm simulated annealing tournament selection multiple filter-based rankers feature selection hybrid filter-wrapper high-dimensionality imbalanced class distribution

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QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm

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SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020年第11期31卷 803-814页

作者： Algamal, Z. Y. Qasim, M. K. Lee, M. H. Ali, H. T. M. Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Gen Sci Mosul Iraq Univ Teknol Malaysia Dept Math Sci Fac Sci Johor Baharu Malaysia Nawroz Univ Coll Comp & Informat Technol Dahuk Iraq

High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSE(test)compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC(50)values of neuraminidase inhibitors that have not been experimentally tested.

关键词： grasshopper optimization algorithm evolutionary algorithm QSAR descriptor selection neuraminidase inhibitor

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Novel variants of grasshopper optimization algorithm to solve numerical problems and demand side management in smart grids

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第10期56卷 10679-10732页

作者： Badr, Azzahraa A. Saafan, Mahmoud M. Abdelsalam, Mohamed M. Haikal, Amira Y. Mansoura Univ Fac Engn Comp & Control Syst Engn Dept Mansoura Egypt

The grasshopper optimization algorithm (GOA), which is one of the recent metaheuristic optimization algorithms, mimics the natural movements of grasshoppers in swarms seeking food sources. Some deficiencies have existed in the original GOA such as slow convergence speed, and the original GOA may get quickly stuck into local solutions facing some complex. For tackling these drawbacks of the original GOA, enhanced versions of GOA have been proposed to deal with the optimization problems more effectively. In the current study, two strategies have been integrated into GOA: the grouping mechanism of non-linear 'c' parameters and the mutation mechanism. Moreover, two different groups of non-linear 'c' parameters have been suggested in the grouping mechanism. Incorporating the grouping mechanism into GOA can update the grasshoppers' positions within a limited local area, whereas the diversity of agents can be improved by integrating the mutation mechanism. Eight Novel-Variants GOA (NVGOAs) are proposed to address the deficiencies of the original GOA. Where two variants NVGOA1_1 and NVGOA2_1 represent the impact of each proposed group of 'c' parameters. Another two variants NVGOA3 and NVGOA4 represent the impact of the mutation mechanism with two different values of probability. Moreover, four variants: NVGOA1_2, NVGOA1_3, NVGOA2_2, and NVGOA2_3 represent the combination of the two proposed mechanisms. First, the comparison between the performance of the proposed variants and the original GOA has been conducted. Then, for validation of the efficiency of the proposed NVGOAs, the performance of the best-recorded NVGOA variants has been tested against the 29 CEC-2017 benchmark functions and compared with six state-of-the-art optimization algorithms based on the mean and the standard deviation metrics. Moreover, the Wilcoxon Signed-Rank test has been employed to exhibit the efficiency of the proposed variants. As well comparative analysis with previous enhancements of GOA has b

关键词： grasshopper optimization algorithm Novel Variants Smart electrical grid Metaheuristics optimization Demand side management

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Chaotic grasshopper optimization algorithm for global optimization

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NEURAL COMPUTING & APPLICATIONS 2019年第8期31卷 4385-4405页

作者： Arora, Sankalap Anand, Priyanka DAV Univ Jalandhar Punjab India Lovely Profess Univ Jalandhar Punjab India

grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.

关键词： grasshopper optimization algorithm Chaotic maps Global optimization problem Multimodal function

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Multi-objective optimal power flow using grasshopper optimization algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第2期45卷 623-645页

作者： Mandal, Barun Roy, Provas Kumar Kalyani Govt Engn Coll Dept Elect Engn Kalyani India Kalyani Govt Engn Coll Dept Elect Engn Kalyani West Bengal India

This paper introduces grasshopper optimization algorithm to efficiently prove its superiority in the optimal power flow problem. To demonstrate the efficiency of the proposed algorithm, it is implemented on the standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with different objectives that reveal presentation indices of the power system. Twelve different cases, in single and multi-objective optimization space, are considered on different curves of fuel cost, environmental pollution emission, voltage profile, and active power loss. The simulation results obtained from grasshopper optimization algorithm techniques are compared to other new evolutionary optimization methods surfaced in the current state-of-the-art literature. It is revealed that the proposed approach secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence speed. The results obtained in this work demonstrate that the grasshopper optimization algorithm method can successfully be applied to solve the non-linear problems connected to power systems. 1. This paper introduces grasshopper optimization algorithm (GOA) to efficiently prove its superiority in the optimal power flow problem (OPF). 2. GOA is implemented on the standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with different objectives. 3. It is revealed that the proposed GOA secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence ***

关键词： backtracking search optimization algorithm emission grasshopper optimization algorithm multi-fuels multi-objective optimal power flow valve-point effect voltage deviation

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Optimal sizing of an autonomous photovoltaic/wind/battery/diesel generator microgrid using grasshopper optimization algorithm

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SOLAR ENERGY 2019年 188卷 685-696页

作者： Bukar, Abba Lawan Tan, Chee Wei Lau, Kwan Yiew UTM Fac Engn Sch Elect Engn Div Elect Power Engn Skudai 81310 Johor Malaysia UTM Fac Engn Sch Elect Engn Inst High Voltage & High Current Johor Baharu 81310 Malaysia Univ Maiduguri Fac Engn Dept Elect & Elect Engn PMB 1069 Maiduguri Borno State Nigeria

This article focuses on the application of a latest nature-inspired metaheuristic optimization algorithm named grasshopper optimization algorithm (GOA) in the area of microgrid system sizing design problem. The proposed algorithm is applied to an autonomous microgrid system in order to determine the optimal system configuration that will supply energy demand reliably based on the deficiency of power supply probability (DPSP) and cost of energy (COE). Firstly, a robust rule-based energy management scheme (EMS) is proposed to coordinate the power flow among the various system components that formed the microgrid. Then, the GOA is integrated with the EMS to perform the optimal sizing for the hybrid autonomous microgrid for five units of residential in an off-grid location in Yobe State, Nigeria. The proposed microgrid comprises of photovoltaic modules, wind turbine, battery storage system and a diesel generator. The effectiveness of the proposed GOA in solving the optimization problem is examined and its performance is compared with particle swarm optimization (PSO) and cuckoo search (CS) optimization algorithm. In addition, a sensitivity analysis is performed on the COE to highlight the impact of varying sensitive system inputs. The proposed optimization is programmed using MATLAB simulation package. The simulation results confirm that GOA is able to optimally size the system as compared to its counterparts, CS and PSO. In which, a decrement of 14% and 19.3% is achieved in the system capital cost, respectively.

关键词： grasshopper optimization algorithm Energy management Wind turbine Photovoltaic Optimal sizing Rule-based

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The structural weight design method based on the modified grasshopper optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第21期81卷 29977-30005页

作者： Ye, Yin Xiong, Shengwu Dong, Chen Chen, Zhenyi Wuhan Univ Technol Sch Comp Sci & Artificial Intelligence Wuhan 430070 Peoples R China Wuhan Univ Technol Sanya Sci & Educ Innovat Pk Sanya 572000 Peoples R China Fuzhou Univ Coll Comp Sci & Big Data Fuzhou 350116 Peoples R China Univ S Florida Dept Elect Engn Tampa FL 33620 USA

Structural weight design is essential and difficult in engineering structure optimization. The design is affected by many factors and belongs to the NP problem. Swarm intelligent algorithm provides a valid way to solve the NP problem. grasshopper optimization algorithm (GOA) is a nature-inspired algorithm that mimics the swarming behaviors of grasshopper insects, but the original GOA has two main problems: the convergence rate is slow and the convergence accuracy is poor. We propose a novel grasshopper optimization algorithm (CV-GOA) consisting of chaos strategy and velocity perturbation mechanism to improve the performance of standard GOA. In CV-GOA, the initial artificial swarm is constructed by Logistic map to increase the diversity of the population and improve the feasibility of finding the global optimal solution;then a set of the velocity vector is introduced and the velocity perturbation mechanism is used to update the velocity of grasshoppers and disturbs the position of grasshoppers, it can improve the searching speed of the algorithm and help the algorithm jump out of the local optimal trap, and improve the optimization accuracy of the algorithm. Experiments are conducted on fifteen benchmark functions to test the accuracy and convergence rate of CV-GOA. Experiments show the proposed CV-GOA achieves higher precision and better convergence rate than other variants. In addition, three structural weight design problems are optimized by CV-GOA, they are cantilever beam design problem, pressure vessel design problem and speed reducer design problem. The results indicate structural weight is designed with superiority. It also proves the effectiveness and value of the proposed algorithm.

关键词： Structural weight design grasshopper optimization algorithm Function optimization Intelligent computing

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Multi-area economic dispatching using improved grasshopper optimization algorithm

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EVOLVING SYSTEMS 2021年第3期12卷 837-847页

作者： Zhang, Peng Ma, Weiwei Dong, Yanli Rouyendegh, Babak Daneshvar Weifang Univ Sci & Technol Dept Business Adm Shouguang 262700 Peoples R China Ankara Yildirim Beyazit Univ Dept Ind Engn TR-06010 Ankara Turkey

In this study a new optimization algorithm is presented to solve the multi-area economic dispatching (MAED) problem. The studied system includes the tie-line constraints including transmission losses, prohibited operating zones, multiple fuels, and valve-point loading. For optimized solving the MAED algorithm, grasshopper optimization algorithm (GOA) is utilized. GOA is a newly introduced swarm-based optimization algorithm that is inspired by the swarming behavior of the grasshopper insects. For validating the proposed method, it is compared with three different case studies and the results have been compared with some different meta-heuristics such as particle swarm optimization, artificial bee colony, evolutionary programming, and differential evolution to demonstrate the capability of the presented system to solve the MAED problems.

关键词： Multi-area economic dispatch grasshopper optimization algorithm Logistic map Tie line constraints

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