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12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)

作者： Ghavifekr, Amir Aminzadeh Mohammadzadeh, Ardashir Ardashir, Jaber Fallah Univ Tabriz Dept Elect & Comp Engn Tabriz Iran Univ Bonab Dept Elect Engn Bonab Iran Islamic Azad Univ Dept Elect Tabriz Branch Tabriz Iran

ISBN: (纸本)9781665403665

Economic designing of microgrids is considered as a multivariable optimization problem. There are several practical remarks in protection, siting and energy management of microgrids. To provide the minimum investment with full operation of DG units, sizing optimization methods should be taken into account. In this paper, modified grasshopper optimization algorithm (MGOA) has been applied to find a solution for the economic dispatch(ED) problem and reconfigure the network structure of microgrids. Cost of energy (COE) and deficiency of power supply probability (DPSP) have been integrated to define an objective function for sizing problem. Simulation results demonstrate the capability of this algorithm to size the autonomous microgrid systems in compare with the similar metaheuristic approaches. It has fast convergence and sustainable balance between exploitation and exploration.

关键词： Microgrids grasshopper optimization algorithm Optimal placement Sizing

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Optimal Allocation of Hybrid Solar-Wind Distributed Generations in Distribution Networks Considering the Uncertainty Using grasshopper optimization algorithm 21

Optimal Allocation of Hybrid Solar-Wind Distributed Generati...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Ramadan, Ashraf Ebeed, Mohamed Kamel, Salah Nasrat, Loai King Saud Univ Gen Adm Projects Riyadh Saudi Arabia Sohag Univ Fac Engn Dept Elect Engn Sohag Egypt Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt

ISBN: (纸本)9781728152899

In this paper the optimal ratings and locations of hybrid renewable energy resources (RER) including solar and wind based distributed generators (DGs) are assigned by an efficient algorithm called grasshopper optimization algorithm (GOA) in radial distribution grid (RDG). The continuous variation of the load in RDG due to variation of the consumer activities is considered as an uncertainty issue. Thus, RER are determined optimally for minimizing the expected power loss considering the uncertainty of load. Assessment of the optimal inclusion the RER using GOA considering load uncertainty of is test on a real distribution system of the East Delta Network as a part of the Unified Egyptian Network. The simulation results show that by incorporating the solar Photovoltaic (PV) and wind systems can minimize the expected power loss considerably. In addition of that the results demonstrate the effectiveness of the GOA for solving the allocation problem of the RER in RDG in term of the power losses.

关键词： DG grasshopper optimization algorithm Distribution Systems renewable energy resources uncertainties

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Research on Network Intrusion Detection Based on Support Vector Machine Optimized with grasshopper optimization algorithm 10

Research on Network Intrusion Detection Based on Support Vec...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Sun, Yiheng Sun, Shuang Zhan, Sikai Yu, Han Yao, Quanfeng Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China Fujian Prov Key Lab Data Intens Comp Quanzhou Peoples R China Fujian Prov Univ Key Lab Intelligent Comp & Informat Proc Fuzhou Peoples R China Wuhan FiberHome Tech Serv Co Ltd Wuhan Peoples R China

ISBN: (纸本)9781728140698

As one of the most important parts of network security, more significance is attached to intrusion detection system (IDS). Numerous techniques including support vector machine (SVM) have been applied to the intrusion detection. However, many methods are utilized to improve the original SVM whose performance is markedly depended on its kernel parameters. Evolutionary algorithms such as genetic algorithm (GA) and particle swarm algorithm (PSO) are also employed to search better kernel parameters while the traditional optimization methods are vulnerable to fall into local minima with slow speed of convergence. In order to improve the precision of SVM in intrusion detection, the support vector machine based on grasshopper optimization algorithm (GOA-SVM) is proposed in the paper. To verify the practicality of the proposed method, several contrast experiments have been carried out using tool of Matlab. The experimental results finally demonstrates the superior performance of the proposed method on intrusion detection.

关键词： component intrusion detection support vector machine grasshopper optimization algorithm

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Multi-objective grasshopper optimization algorithm for Robot Path Planning in Static Environments 19

Multi-objective Grasshopper Optimization Algorithm for Robot...

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19th IEEE International Conference on Industrial Technologies (ICIT)

作者： Elmi, Zahra Efe, Mehmet Onder Hacettepe Univ Dept Comp Engn Autonomous Syst Lab Ankara Turkey

ISBN: (纸本)9781509059492

Finding the most appropriate path in robot navigation has been an interesting challenge in recent years. A number of different techniques have been proposed to address this problem. Heuristic methods are one of them that have been efficiently used in many complex and multi-dimensional optimization problems. In this paper, we present a new algorithm for robot path planning in a static environment. The main aim is to use a multi objective method to minimize several metrics such as cost, distance, energy or time. Distance, path smoothness and robot path planning time is optimized in the current work. The contribution of this work is to calculate an appropriate fitness function at each iteration to achieve the best solution. The obtained result is compared with the Particle Swarm optimization (PSO) algorithm. The proposed algorithm displays better performance characteristics in terms of time and path smoothness than PSO algorithm and the obtained path lengths are shorter than those obtained with PSO.

关键词： Path Planning Mobile Multi-Robot grasshopper optimization algorithm Obstacle Avoidance Static Environment

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A Multi-strategy Improved grasshopper optimization algorithm for Solving Global optimization and Engineering Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 182-182页

作者： Liu, Wei Yan, Wenlv Li, Tong Han, Guangyu Ren, Tengteng Shenyang Ligong Univ Coll Informat Sci & Engn Shenyang 110168 Peoples R China

This paper presents a multi-strategy improved grasshopper optimization algorithm (MSIGOA), which aims to address the shortcomings of the grasshopper optimization algorithm (GOA), including its slow convergence, vulnerability to trapping into local optima, and low accuracy. Firstly, to improve the uniformity of the population distribution in the search space, the MSIGOA uses circle mapping for the population initialization. A nonlinear decreasing coefficient is utilized instead of an original linear decreasing coefficient to improve the local exploitation and global exploration capabilities. Then, the modified golden sine mechanism is added during the position update stage to change the single position update mode of GOA and enhance the local exploitation capability. The greedy strategy is added to greedily select the new and old positions of the individual to retain a better position and increase the speed of convergence. Finally, the quasi-reflection-based learning mechanism is utilized to construct new populations to improve population multiplicity and the capability to escape from the local optima. This paper verifies the efficacy of MSIGOA by comparing it with other advanced algorithms on six engineering design problems, CEC2017 test functions, and 12 classical benchmark functions. The experimental results show that MSIGOA performs better than the original GOA and other compared algorithms and has stronger comprehensive optimization capabilities.

关键词： grasshopper optimization algorithm Circle mapping Nonlinear decreasing coefficient Golden sine algorithm Quasi-reflection-based learning

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Modified grasshopper optimization algorithm for detection of Autism Spectrum Disorder

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PHYSICAL COMMUNICATION 2020年 41卷 101115-101115页

作者： Goel, Nikita Grover, Bhavya Anuj Gupta, Deepak Khanna, Ashish Sharma, Moolchand GGSIPU Univ Maharaja Agrasen Inst Technol Delhi India

Autism Spectrum Disorder (ASD) is a disorder of neurodevelopment whose delayed diagnosis has been posing a barrier in alleviating the severity of the conditions of the sufferers. ASD patients experience difficulties with social communication and interaction. They also show restricted and repetitive patterns of behavior. Approximately 62 million people are diagnosed with ASD globally. Males are about 3–4 times more likely to suffer from ASD than females. Statistically, ASD can be detected between the age of one to two, but some cases may remain undetected for a substantial period. It is crucial to detect ASD precisely and at the nascent stage to remediate the disease. The presented paper proposes an algorithm, namely Modified grasshopper optimization algorithm (MGOA), capable of detecting Autism Spectrum Disorder at all stages of life. GOA is a nature-inspired algorithm that has the potential to explore and exploit the search space effectively. Through this paper, we have attempted to overcome the shortcomings of the traditional GOA, resulting in early diagnosis of the disease. The algorithm is used on the three ASD screening datasets targeting different age groups, namely children, adolescents, and adults, are used for numerical experimentation, and the results are contrasted with the state-of-the-art algorithms. The proposed algorithm with the Random Forest classifier predicted ASD with an approximate accuracy of 100% with specificity and sensitivity as 100% at all stages of life.

关键词： Autism Spectrum Disorders grasshopper optimization algorithm Random Forest Machine learning

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A Novel Network Architecture of Decision-Making for Self-Driving Vehicles Based on Long Short-Term Memory and grasshopper optimization algorithm

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IEEE ACCESS 2020年 8卷 155429-155440页

作者： Shi, Yunxia Li, Ying Fan, Jiahao Wang, Tan Yin, Taiqiao Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Space Technol Jilin Co Ltd Jilin 132013 Jilin Peoples R China Jilin Univ Coll Software Changchun 130012 Peoples R China

Long short-term memory network is one of the most important network architectures of decision-making for self-driving vehicles. Nevertheless, the decision-making accuracy of long short-term memory network is limited, the information of the surrounding vehicles is not taken into consideration, which is critical for the decision-making of the ego vehicle, and the classification capability of long short-term memory network is poor. In this article, a novel network architecture called improved long short-term memory network with support vector machine classifier optimized by grasshopper optimization algorithm (GOA-ImLSTM) is proposed. Three improvements are presented in GOA-ImLSTM. Firstly, to consider the information of the surrounding vehicles, a new network architecture, used to extract vital features for self-driving vehicles, with three parallel long short-term memory network units and a network unit serial connected according to vehicle location is designed. Secondly, to improve classification accuracy, support vector machine with stronger classification capability than softmax is introduced to accomplish the classification task. Thirdly, to promote the classification capability of support vector machine, grasshopper optimization algorithm is employed to optimize the parameters of support vector machine. Moreover, to balance exploration and exploitation ability of grasshopper optimization algorithm, dynamic weights in position movement formula are defined. The experiments indicate that GOA-ImLSTM improves the accuracy of results compared with other decision-making methods for self-driving vehicles on the Next Generation SIMulation.

关键词： Support vector machines Decision making optimization Feature extraction Logic gates Hidden Markov models Classification algorithms grasshopper optimization algorithm long short-term memory self-driving decision-making support vector machine

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Distributed energy resource allocation using multi-objective grasshopper optimization algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 201卷 107564-107564页

作者： Ahmadi, Bahman Ceylan, Oguzhan Ozdemir, Aydogan Istanbul Tech Univ Dept Elect Engn Istanbul Turkey Kadir Has Univ Management & Informat Syst Dept Istanbul Turkey

The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective grasshopper optimization algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices.

关键词： Distribution network planning Optimal planning Photovoltaic generation Wind energy generation Battery energy storage system grasshopper optimization algorithm

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Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2021年 208卷 104196-104196页

作者： Algamal, Zakariya Yahya Qasim, Maimoonah Khalid Lee, Muhammad Hisyam Ali, Haithem Taha Mohammad Univ Mosul Dept Stat & Informat Mosul Iraq Univ Mosul Dept Gen Sci Mosul Iraq Univ Teknol Malaysia Fac Sci Dept Math Sci Johor Baharu Malaysia Nawroz Univ Coll Comp & Informat Technol Dahuk Kurdistan Regio Iraq

High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems. However, it is usually needed to tune manually the *** addition, SVR cannot perform feature selection. Nature-inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, an improving grasshopper optimization algorithm (GOA) is proposed by adapting a new function of the main controlling parameter of GOA to enhance the exploration and exploitation capability of GOA. This improving is utilized to optimize the hyperparameters of the SVR with embedding the feature selection simultaneously. Experimental results, obtained by running on four datasets, show that our proposed algorithm performs better than crossvalidation method, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the proposed improving confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared to other nature-inspired algorithms, which proves the ability of GOA in searching for the best hyperparameters values and selecting the most informative features for prediction tasks.

关键词： grasshopper optimization algorithm Evolutionary algorithm SVR Feature selection

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Integrating grasshopper optimization algorithm with Local Search for Solving Data Clustering Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 783-793页

作者： El-Shorbagy, M. A. Ayoub, A. Y. Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Menoufia Univ Fac Engn Shebin Kom Dept Basic Engn Sci Shibin Al Kawm Egypt

This paper proposes a hybrid approach for solving data clustering problems. This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems. in addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering. The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution. While the second stage aims by LS to increase LS performance and obtain the best optimal solution. In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS. In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique. The results of the proposed methodology are compared to previous studies;where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： Data clustering problems grasshopper optimization algorithm local search optimization swarm intelligence algorithms

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