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Efficient feature selection method using real-valued grasshopper optimization algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2019年 119卷 61-72页

作者： Zakeri, Arezoo Hokmabadi, Alireza Islamic Azad Univ Aliabad Katoul Branch Dept Biomed Engn Fac Engn Aliabad Katoul *** Iran Islamic Azad Univ Aliabad Katoul Branch Dept Elect Engn Fac Engn Aliabad Katoul *** Iran

Feature selection is the problem of finding the minimum number of features among a redundant feature space which leads to the maximum classification performance. In this paper, we have proposed a novel feature selection method based on mathematical model of interaction between grasshoppers in finding food sources. Some modifications were applied to the grasshopper optimization algorithm (GOA) to make it suitable for a feature selection problem. The method, abbreviated as GOFS is supplemented by statistical measures during iterations to replace the duplicate features with the most promising features. Several publicly available datasets with various dimensionalities, number of instances, and target classes were considered to evaluate the performance of the GOFS algorithm. The results of implementing twelve well-known and recent feature selection methods were presented and compared with GOFS algorithm. Comparative experiments indicate the significance of the proposed method in comparison with other feature selection methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Feature selection grasshopper optimization algorithm Meta-heuristic algorithms Pattern recognition

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Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第21-23期80卷 31569-31597页

作者： Ziadeh, Ahmad Abualigah, Laith Abd Elaziz, Mohamed Sahin, Canan Batur Almazroi, Abdulwahab Ali Omari, Mahmoud Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zagazig Univ Dept Math Fac Sci Zagazig Egypt Malatya Turgut Ozal Univ Fac Engn & Nat Sci Malatya Turkey Univ Jeddah Coll Comp & Informat Technol Khulais Dept Informat Technol Jeddah Saudi Arabia

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid grasshopper optimization algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and grasshopper optimization algorithm. Experimental results are applied in two scenarios;the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26 % enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

关键词： Hybrid Method grasshopper optimization algorithm Differential Evolution Power scheduling Smart homes Smart grid

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Hybrid Artificial Neural Network-based grasshopper optimization algorithm for Anomaly Detection in Wireless Body Area Networks

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IETE JOURNAL OF RESEARCH 2024年第4期70卷 3738-3752页

作者： Thamaraimanalan, T. Ramalingam, S. Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore Tamil Nadu India

Recent technological advances in sensor networks show rapid growth in healthcare monitoring systems based on Wireless Body Area Networks (WBANs). A small sensor attached to the body can record various psychological parameters. WBAN sensors frequently fail due to noise, misalignment of the hardware, and patient perspiration. It is difficult for healthcare professionals to determine whether the data collected by these sensing devices is defected or influenced by a malicious attacker. Even inaccurate information can result in a wrong diagnosis, and the physiological parameters are categorized as normal or abnormal. This research focuses on reading anomaly detection for wireless body sensors and proposes a novel method based on hybrid machine-learning algorithms. A hybrid Artificial Neural Network - grasshopper optimization algorithm (ANN-GOA) is proposed to find anomalous records in the dataset. Each anomalous record's predicted value determines whether the recognized form is anomalous. The suggested hybrid ANN-GOA method results are compared with the parameters like recall, accuracy, F1 score, loss, and precision. The simulation results of the proposed hybrid ANN-GOA algorithm provide better accuracy of 98.8%, recall of 99.9%, F-score of 97.9% and precision of 99.8% when compared with other conventional algorithms.

关键词： Artificial Neural Network grasshopper optimization algorithm healthcare remote patient monitoring wireless body area networks

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An effect of chaos grasshopper optimization algorithm for protection of network infrastructure

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COMPUTER NETWORKS 2020年 176卷 107251-107251页

作者： Dwivedi, Shubhra Vardhan, Manu Tripathi, Sarsij NIT Raipur Dept Comp Sci & Engn Chattisgarh India

Due to the proliferation of sophisticated cyber extortion with exponentially critical effects, intrusion detection system is being evolved systematically their revealing, understanding, attribution and mitigation capabilities. Unfortunately, most of the modern Intrusion Detection System (IDS) technique does not provide sufficient defense services in the wireless environment while maintaining operational continuity and the stability of the defense objective in the presence of intruders and modern attacks. To resolve this problem, we propose a new feature selection technique by combining Ensemble of Feature Selection (EFS) and Chaotic Adaptive grasshopper optimization algorithm (CAGOA) method, called ECAGOA. The proposed method has the capability of preventing stagnation issue and is particularly credited to the following three aspects. Firstly, EFS method is applied for selecting the high ranked subset of attributes. Then, we have employed chaos concept in grasshopper optimization algorithm (GOA) which generates a uniformly distributed population to enhance the quality of the initial populations and has the capability to manage two different issues such as the ability to search for new space termed as exploration and the ability to use existing space termed as exploitation in the optimization process. In order to avoid local optima and premature convergence, lastly, an adaptive grasshopper optimization algorithm is developed by using organized parameter adaptation method. Furthermore, the adaptive behavior of GOA is applied to decide whether a record signifies an anomaly or not, differing from some approaches acquainted in the literature. Support vector machine (SVM) is used as a fitness function in the proposed method to choose the relevant features that can help classify the attacks accurately. In addition, it is also applied to optimize the penalty factor (C), kernel parameter (sigma), and tube size (epsilon) of SVM method. The proposed algorithm is evaluated usin

关键词： ISCX 2012 CIC-IDS2017 Evolutionary algorithm grasshopper optimization algorithm Intrusion detection

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A novel hyperbolic time-delayed grey model with grasshopper optimization algorithm and its applications

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AIN SHAMS ENGINEERING JOURNAL 2021年第1期12卷 865-874页

作者： Xiang, Xiwang Ma, Xin Fang, Yizhu Wu, Wenqing Zhang, Gaoxun Southwest Univ Sci & Technol Sch Sci Mianyang 621010 Sichuan Peoples R China Petrochina Southwest Oil & Gasfield Co Explorat & Dev Res Inst Chengdu 610051 Peoples R China

Discharge for wastewater treatment plays a key role in improving the water quality, thereby guaranteeing living quality of citizens. With high-speed economics growth and economics reforming, total amount of China's discharge of wastewater treatment is sharing high uncertainty, leading to many difficulties in accurate forecasts of discharge of wastewater treatment. Based on grey system theory, the hyperbolic time-delayed term is introduced in this paper to develop a novel forecasting model in order to deal with uncertainties of China's sewage discharge forecasting. The key nonlinear parameter of the proposed model is determined by the grasshopper optimization algorithm. A series of practical numerical cases prove that the proposed model is reliable in comparison with six existing models are used for comparison. Then we apply it to predict the behavior of sewage discharge in China, those results against demonstrating the model our proposed has more satisfactory prediction precision. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： Discharge of sewage Ecological civilization construction Hyperbolic time-delayed grey model grasshopper optimization algorithm Prediction precision

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Recent meta-heuristic grasshopper optimization algorithm for optimal reconfiguration of partially shaded PV array

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SOLAR ENERGY 2018年第Sep.期171卷 638-651页

作者： Fathy, Ahmed Zagazig Univ Elect Power & Machine Dept Fac Engn Zagazig Egypt

When solar radiation striking the PV modules inserted in PV array is non-homogeneous;then the partial shading operation is taken place. This phenomenon has negative effects on the efficiency of the PV array as the generated power is reduced, hot spots are generated;one solution to get rid of these negatives is the reconfiguration of modules such that distributing the solar radiation in regular form as much as possible. Therefore, this paper proposed a new methodology based on recent meta-heuristic optimization algorithm named grasshopper to be applicable in solving the reconfiguration process of the partially shaded PV array optimally. The main purpose of such action is to maximize the power extracted from the array via proposed objective function presented in this work. The PV array arrangement obtained via the proposed approach incorporated recent meta-heuristic grasshopper optimization algorithm (GOA) is compared with those obtained via total cross tied connection (TCT), Su Do Ku connection and genetic algorithm (GA) configuration. Additionally, different shadow patterns during a day are studied and reconfiguration arrangement is obtained at each hour. The obtained results confirm the reliability and the efficiency of the proposed GOA in evaluating the global maximum power point (GMPP) extracted from the partially shaded PV array.

关键词： Partially shaded PV array Global maximum power grasshopper optimization algorithm

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A comprehensive survey of the grasshopper optimization algorithm: results, variants, and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第19期32卷 15533-15556页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 Saadiyat Island U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

关键词： grasshopper optimization algorithm Meta-heuristic optimization algorithms optimization problems Bio-inspired algorithms

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Many-Objective grasshopper optimization algorithm (MaOGOA): A New Many-Objective optimization Technique for Solving Engineering Design Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-34页

作者： Kalita, Kanak Jangir, Pradeep Cep, Robert Pandya, Sundaram B. Abualigah, Laith Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Mech Engn Avadi 600062 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Dept Bioinformat Chennai 602105 India Jadara Univ Res Ctr Irbid 21110 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Tech Univ Ostrava Fac Mech Engn Dept Machining Assembly & Engn Metrol Ostrava 70800 Czech Republic Shri KJ Polytech Dept Elect Engn Bharuch 392001 India Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Middle East Univ MEU Res Unit Amman 11831 Jordan Chitkara Univ Ctr Res Impact & Outcome Rajpura 140401 India Univ Tabuk Artificial Intelligence & Sensing Technol AIST Res Ctr Tabuk 71491 Saudi Arabia

In metaheuristic multi-objective optimization, the term effectiveness is used to describe the performance of a metaheuristic algorithm in achieving two main goals-converging its solutions towards the Pareto front and ensuring these solutions are well-spread across the front. Achieving these objectives is particularly challenging in optimization problems with more than three objectives, known as many-objective optimization problems. Multi-objective algorithms often fall short in exerting adequate selection pressure towards the Pareto front in these scenarios and difficult to keep solutions evenly distributed, especially in cases with irregular Pareto fronts. In this study, the focus is on overcoming these challenges by developing an innovative and efficient a novel Many-Objective grasshopper Optimisation algorithm (MaOGOA). MaOGOA incorporates reference point, niche preserve and information feedback mechanism (IFM) for superior convergence and diversity. A comprehensive array of quality metrics is utilized to characterize the preferred attributes of Pareto Front approximations, focusing on convergence, uniformity and expansiveness diversity in terms of IGD, HV and RT metrics. It acknowledged that MaOGOA algorithm is efficient for many-objective optimization challenges. These findings confirm the approach effectiveness and competitive performance. The MaOGOA efficiency is thoroughly examined on WFG1-WFG9 benchmark problem with 5, 7 and 9 objectives and five real-world (RWMaOP1- RWMaOP5) problem, contrasting it with MaOSCA, MaOPSO, MOEA/DD, NSGA-III, KnEA, RvEA and GrEA algorithms. The findings demonstrate MaOGOA superior performance against these algorithms.

关键词： Many-objective optimization grasshopper optimization algorithm Reference point strategies Information feedback mechanism Diversity maintenance

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An improved grasshopper optimization algorithm based on dynamic dual elite learning and sinusoidal mutation

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COMPUTING 2022年第5期104卷 981-1015页

作者： Chen, Lei Tian, Yu Ma, Yunpeng Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Sci Tianjin Peoples R China

grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms.

关键词： grasshopper optimization algorithm Dynamic elite learning Sinusoidal mutation optimization algorithm Meta-heuristic algorithm

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Multi-objective grasshopper optimization algorithm based on multi-group and co-evolution

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2021年第3期18卷 2527-2561页

作者： Wang, Chao Li, Jian Rao, Haidi Chen, Aiwen Jiao, Jun Zou, Nengfeng Gu, Lichuan Anhui Agr Univ Hefei 230036 Peoples R China Minist Agr Key Lab Agr Elect Commerce Hefei 230036 Peoples R China

The balance between exploration and exploitation is critical to the performance of a Meta-heuristic optimization method. At different stages, a proper tradeoff between exploration and exploitation can drive the search process towards better performance. This paper develops a multi-objective grasshopper optimization algorithm (MOGOA) with a new proposed framework called the Multi-group and Co-evolution Framework which can archive a fine balance between exploration and exploitation. For the purpose, a grouping mechanism and a co-evolution mechanism are designed and integrated into the framework for ameliorating the convergence and the diversity of multi-objective optimization solutions and keeping the exploration and exploitation of swarm intelligence algorithm in balance. The grouping mechanism is employed to improve the diversity of search agents for increasing coverage of search space. The co-evolution mechanism is used to improve the convergence to the true Pareto optimal front by the interaction of search agents. Quantitative and qualitative outcomes prove that the framework prominently ameliorate the convergence accuracy and convergence speed of MOGOA. The performance of the presented algorithm has been benchmarked by several standard test functions, such as CEC2009, ZDT and DTLZ. The diversity and convergence of the obtained multi-objective optimization solutions are quantitatively and qualitatively compared with the original MOGOA by using two performance indicators (GD and IGD). The results on test suits show that the diversity and convergence of the obtained solutions are significantly improved. On several test functions, some statistical indicators are more than doubled. The validity of the results has been verified by the Wilcoxon rank-sum test.

关键词： multi-objective optimization meta-heuristics swam intelligence algorithm grasshopper optimization algorithm

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