Grasshopper Optimization Algorithm Algorithm Research Articles

A Hybrid Bio-Inspired Optimization Scheme for RSU Distribution in Vehicular Ad-Hoc Network

—Vehicular Ad-Hoc Network (VANETs) is a trending modelthat plays maximum role inall the application of Intelligent Transport Systems (ITS). In general, VANETs are based on two communication types such as among vehicles and vehicles to infrastructure Roadside Units(RSU) based communication. In this paper, HACOGO approach is developedbased on ahybrid bio inspired optimization with the combination of Ant Colony Optimization (ACO) with Grasshopper Optimization Algorithm (GOA). The HACOGO is used to perform stable RSU distribution in VANETs. The ACO algorithm is used to help the vehicle to select the optimal path toward the destination and GOA algorithm highly magnificent vehicle is chosen as RSU. The Performance analysis of HACOGO is done by calculating the common parameters such as packet delivery ratio, end-to-end delay, packet loss and routing overhead. To analysis the effectiveness of the proposed model its results are compared with the earlier works. From the outcome it is proven using the HACOGO approach the end-to-end delay, packet loss and routing overhead is reduced as well as the packet delivery ratio of the network is increased than others. Keywords—Vehicular Ad-Hoc Network,Roadside Units, Ant Colony Optimization.

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

SummaryDue to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy‐based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean‐Average Precision, F‐measure, Precision‐recall, and Precision rank are evaluated and analyzed by using TREC‐COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state‐of‐art works. For a total of 60 queries, the proposed model offers an F1‐Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1‐score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

Grasshopper optimization algorithm based path planning for autonomous mobile robot

Autonomous mobile robots have become very popular and essential in our life, especially in industry. One of the crucial activities of the robot is planning the path from a start point to a target point, avoiding obstacles in the environment. Recently, path planning received more attention, and many methodologies have been proposed. Path planning studies have shown the effectiveness of swarm intelligence in complex and known or unknown environments. This paper presents a global path planning method based on grasshopper optimization algorithm (GOA) in a known static environment. This algorithm is improved using the bias factor to increase the efficiency and improve the resulting path. The resulting path from this algorithm is further enhanced using an improved version multinomial logistic regression algorithm (MLR). The algorithms were evaluated using three different large environments of varying complexities. The GOA algorithm has been compared with the ant colony optimization algorithm (ACO) using the same environments. The experiments have shown the superiority of our algorithm in terms of time convergence and cost.

Improving precipitable water vapor estimations of the Moderate Resolution Imaging Spectroradiometer (MODIS) using metaheuristic algorithms

Extracting Total Precipitable Water Vapor (TPW) and its variations from the Moderate Resolution Imaging Spectroradiometer (MODIS) data on a local scale is challenging. Meta-heuristic algorithms have a high ability to solve complex engineering problems of hydrology and climatic studies. Hence, the present study extracts TPW values using meta-heuristic algorithms for MODIS surface reflectance data. In this regard, radiosonde observations of six stations in the western part of Iran during 2019 and 2020 were used as the ground truth data for training algorithms and evaluating the results. The results were also validated with Global Positioning System (GPS) TPW data of Tehran-Mehrabad station. Moreover, MODIS surface reflectance (MOD021) and cloud mask (MOD35) products were prepared to distinguish cloudy days from non-cloudy, along with MODIS TPW (MOD05) for corresponding dates. Meta-heuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search (HS), Invasive Weed Optimization (IWO), Gray Wolf Optimization (GWO), and Grasshopper Optimization Algorithm (GOA), were applied to improve the performance of MOD05 algorithm. The results showed the proper performance of all the selected algorithms. Nevertheless, the GOA algorithm acquired the best result. The Root Mean Square Error (RMSE) and R values obtained from comparing GOA results with radiosonde data were 2.96 mm and 0.82. Those values for comparing GOA results with GPS data were 2.94 mm and 0.83, respectively. However, MODIS TPW, RMSE, and R showed 6.53 mm and 0.69, respectively, compared to radiosonde data and 6.55 mm and 0.68 compared to GPS data. Overall, the results proved the proper performance of the used meta-heuristic algorithms in extracting TPW from MODIS measurements on a local scale.

A New Multi-Level Semi-Supervised Learning Approach for Network Intrusion Detection System Based on the ‘GOA’

One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical [Formula: see text]-means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN’s accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously.

Grass Hopper Optimization Algorithm for Off-Grid Rural Electrification of an Integrated Renewable Energy System

A suitable alternative to grid expansion has been found in renewable energy sources like wind, solar, and biomass. To put it another way, relying solely on one of the major renewable sources is both inefficient and expensive. As a result, an integrated renewable energy system is a viable option. The purpose of this article is to discuss the use of the Grasshopper Optimization Algorithm (GOA) for renewable energy sizing in the current study area. For an autonomous microgrid network, the proposed technique finds the optimum system size on the basis of Loss of Power Supply Probability (LPSP). The proposed microgrid consists of PV panels, wind turbines, biomass generator and a battery storage system. The proposed GOA algorithm’s convergence efficiency in resolving the current optimization problem is investigated and compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in the MATLAB software environment. The simulation results show that the GOA algorithm outperforms its counterparts, GA and PSO, in terms of system sizing.

Analysis of GOA optimized two-stage controller for frequency regulation of grid integrated virtual power plant

Renewable energy has been employed to reduce greenhouse emissions and global warming. VPP is an example of renewable energy integration technology that enables users to tap into unlimited renewable energy supplies while reducing their reliance on traditional power sources. This research looks at a synchronized control grid integrated VPP with parabolic trough solar collector thermal system (PTSCTS), a wind generator (WG), and an electric vehicle (EV). An active power control strategy on an interconnected microgrid in revised form is attempted taking into consideration the communication delay. The proportional integrator derivative (PID) and two-stage (PI)-(1+PD) controllers are used in the proposed control strategy. Firefly algorithm (FA), butterfly optimization algorithm (BOA), particle swarm optimization (PSO), and recent Grasshopper optimization algorithm (GOA) have been used for optimizing the control parameters of the controllers. The analysis shows that the new GOA algorithm performs better in terms of control strategy than other optimization tools such as FA, BOA, and PSO. The sensitivity analysis of the proposed controller under different conditions has been done, and it has been explored that the proposed Two-stage PI-(1 + PD) controller parameters are very robust in dealing with the parametric variations of the integrated VPP model.

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

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.

Application of Grasshopper Optimization Algorithm for Selective Harmonics Elimination in Low-Frequency Voltage Source Inverter

In this paper, an application of the recently developed Grasshopper Optimization Algorithm (GOA) for calculation of switching angles for Selective Harmonic Elimination (SHE) PWM in low-frequency voltage source inverter is proposed. The algorithm is based on insect behavior in the food foraging swarm of grasshoppers. The characteristic feature of GOA is the movement of agents is based on the position of all agents in the swarm. This method represents a higher probability of convergence than Particle Swarm Optimization (PSO) Modifications of GOA have been examined regarding their effect on the algorithm’s convergence. The proposed modifications were based on the following techniques: Grey Wolf Optimizer (GWO), Natural Selection (NS), Adaptive Grasshopper Optimization Algorithm (AGOA), and Opposite Based Learning (OBL). The performance of GOA and its modifications were compared with well-known PSO. Areas, where GOA is superior to PSO in terms of probability of convergence, have been shown. The efficiency of the GOA algorithm applied for solving the SHE problem was confirmed by measurements in the laboratory.

Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering

Text analysis in the field of text mining requires complex techniques for handling several text documents. Text clustering is among the most effective tactics in the field of text mining, machine recruitment and pattern recognition. Computers can start organizing a corpus document in certain organizational structure of conceptual clusters using reasonable text-clustering method. Informative and un-informational functionalities of the text documents contain noisy, inconsequential and superfluous features. The main method of finding a new subset of informative feats for each document is the unsupervised selection of text features. The functional selection technique has two aims: (1) maximize text clustering algorithm reliability, (2) minimize the number of uninformative traits. The proposed technique is that it produces a mature convergence rate and requires minimal computational time and is trapped in local minima in a low dimensional space. The text data is fed as the input and pre-processing steps are performed in the document. Next, the text feature selection is processed by selecting the local optima from the text document and then selecting the best global optima from local optimum using hybrid GWO–GOA.​ Furthermore, the selected optima are clustered using the Fuzzy c-means (FCM) clustering algorithm. This algorithm improves the reliability and minimizes the computational time cost. Eight datasets are used in the proposed algorithm and the performance is envisaged efficaciously. The evaluation metrics used for performing text feature selection and text clustering are accuracy, precision, recall, F-measure, sensitivity, specificity and show better quality when comparing with various other algorithms. When comparing with GWO, GOA and the proposed hybrid GWO–GOA algorithm, the proposed methodology reveals 87.6% of efficiency.

Performance Evaluation of Hybrid Photovoltaic Thermal Thermoelectric Collector Using Grasshopper Optimization Algorithm With Simulated Annealing

Abstract In this paper, a mathematical model of a single-channel photovoltaic thermal (PVT) air collector incorporated with a thermoelectric (TE) module has been presented. The overall electrical energy obtained from the photovoltaic thermal-thermoelectric (PVT-TE) collector is 5.78% higher than the PVT collector. Further, the grasshopper optimization algorithm (GOA) and hybrid grasshopper optimization algorithm with simulated annealing (GOA-SA) have been proposed and implemented to optimize the parameters of opaque PVT-TE collector. Although there are different parameters that influence the performance of PVT-TE system, yet in this study only four parameters, viz., length of the channel (L), width of the channel (b), mass flowrate of air in the channel (mair), and temperature of air at the inlet of channel (Tair,i) are considered for optimization. The simulation result demonstrates that the hybrid GOA-SA algorithm turned out to be an exceptionally effective method for optimal tuning of the parameters of the PVT-TE system. The result explicitly shows that the average value of overall electrical efficiency and exergy gain are 15.27% and 27.0565 W, respectively, when the parameters are optimized by the suggested GOA-SA algorithm which is way ahead with respect to the outcomes obtained with that of the calculated values or using GOA algorithm alone.

An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem

The grasshopper optimization algorithm (GOA) is a novel metaheuristic algorithm. Because of its easy deployment and high accuracy, it is widely used in a variety of industrial scenarios and obtains good solution. But, at the same time, the GOA algorithm has some shortcomings: (1) original linear convergence parameter causes the processes of exploration and exploitation unbalanced; (2) unstable convergence speed; and (3) easy to fall into the local optimum. In this paper, we propose an enhanced grasshopper optimization algorithm (EGOA) using a nonlinear convergence parameter, niche mechanism, and the β-hill climbing technique to overcome the abovementioned shortcomings. In order to evaluate EGOA, we first select the benchmark set of GOA authors to test the performance improvement of EGOA compared to the basic GOA. The analysis includes exploration ability, exploitation ability, and convergence speed. Second, we select the novel CEC2019 benchmark set to test the optimization ability of EGOA in complex problems. According to the analysis of the results of the algorithms in two benchmark sets, it can be found that EGOA performs better than the other five metaheuristic algorithms. In order to further evaluate EGOA, we also apply EGOA to the engineering problem, such as the bin packing problem. We test EGOA and five other metaheuristic algorithms in SchWae2 instance. After analyzing the test results by the Friedman test, we can find that the performance of EGOA is better than other algorithms in bin packing problems.

A Hybrid Grasshopper Optimization Algorithm With Invasive Weed for Global Optimization

The grasshopper optimization algorithm (GOA) is a promising metaheuristic algorithm for optimization. In the current study, a hybrid grasshopper optimization algorithm with invasive weed optimization (IWGOA) is proposed. The invasive weed optimization (IWO) and random walk strategy are helpful for improving the search precision and accelerating the convergence rate. In addition, the exploration and exploitation capability of the IWGOA algorithm are further enhanced by the grouping strategy. The IWGOA algorithm is compared with some typical and latest optimization algorithms including genetic algorithm (GA), moth-flame optimization algorithm (MFO), particle swarm optimization and gravitational search algorithm (PSOGSA), ant lion optimizer (ALO), conventional GOA algorithm, chaotic GOA algorithm (CGOA) and opposition-based learning GOA algorithm (OBLGOA) on 23 benchmark functions and 30 CEC 2014 benchmark functions. The results show that the IWGOA algorithm is able to provide better outcomes than the other algorithms on the majority of the benchmark functions. Additionally, the IWGOA algorithm is applied to multi-level image segmentation, and obtains promising results. All of these findings demonstrate the superiority of the IWGOA algorithm.

Grasshopper optimization algorithm with principal component analysis for global optimization

As one of the latest meta-heuristic algorithms, the grasshopper optimization algorithm (GOA) has extensive applications because of its efficiency and simplicity. However, the basic GOA still has enough room for improvement. Therefore, a new variant GOA algorithm which combines two strategies, namely PCA–GOA, is proposed. Firstly, principal component analysis strategy is employed to obtain the grasshoppers with minimally correlated variables, which can improve the exploitation capability of the GOA. Then, a novel inertia weight is proposed to balance exploration and exploitation in an intelligent way, which makes the GOA to have better search capability. Furthermore, the performance of PCA–GOA is evaluated by solving a series of benchmark functions. The experimental results manifest that the PCA–GOA provides better outcomes than the basic GOA and other state-of-the-art algorithms on the majority of functions, which demonstrates the superiority of the PCA–GOA.

Improving multilayer perceptron neural network using chaotic grasshopper optimization algorithm to forecast iron ore price volatility

Developing an accurate forecasting model for the volatility of iron ore price plays a vital role in future investments and decisions for mining projects and related companies. Viewed from this perspective, this paper proposes a novel model for accurately forecasting monthly iron ore price volatilities. This model integrates chaotic behavior into a recent meta-heuristic method grasshopper optimization algorithm (GOA) to form a new GOA algorithm called chaotic grasshopper optimization algorithm (CGOA), which is used as a trainer to learn the multilayer perceptron neural network (NN). The results of the proposed model (CGOA–NN) are compared to other models, including the conventional grasshopper optimization algorithm for NN (GOA–NN), Particle swarm optimization for NN (PSO–NN), Genetic Algorithm for NN (GA–NN), and classic NN. Empirical results demonstrate the superiority of the hybrid CGOA–NN model over other models. Moreover, the proposed CGOA–NN model demonstrates an improvement in the forecasting accuracy obtained from classic NN, GA–NN, PSO–NN, and GOA–NN models by 60.82%, 32.18%, 16.49%, and 38.71% decrease in mean square error, respectively.

Improved grasshopper optimization algorithm to solve energy consuming reduction of chiller loading

ABSTRACTThis paper presents a new improved version of grasshopper optimization algorithm (GOA) to find the optimal solution for the optimal chiller loading (OCL) problem subject to obtain the minimum power consumption. To improve the convergence speed of the GOA algorithm, chaos theory is employed. To validate the proposed method, it is compared with three different OCL case studies and the results are compared with GA, WCO, PSO, and original GOA to demonstrate the capability of the presented system. Simulation results showed that the presented chaos GOA gives better (or equal) results compared with other studied methods.

Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

This article presents an integrated approach towards the economical operation of a hybrid system which consists of conventional thermal generators and renewable energy sources like windmills using a grasshopper optimization algorithm (GOA). This is based on the social interaction nature of the grasshopper, considering a carbon tax on the emissions from the thermal unit and uncertainty in wind power availability. The Weibull distribution is used for nonlinearity of wind power availability. A standard system, containing six thermal units and two wind farms, is used for testing the dispatch model of three different loads. The GOA results are compared with those obtained using a recently developed quantum-inspired particle swarm optimization (QPSO) optimization technique available in the literature. The simulation results demonstrate the efficacy and ability of GOA over the QPSO algorithm in terms of convergence rate and minimum fitness value. Performance analysis under wind power integration and emission minimization further confirms the supremacy of the GOA algorithm.

An efficient hybrid multilayer perceptron neural network with grasshopper optimization

This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs.

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

Abstract 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.