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1
Steczek, Marcin, Włodzimierz Jefimowski, and Adam Szeląg. "Application of Grasshopper Optimization Algorithm for Selective Harmonics Elimination in Low-Frequency Voltage Source Inverter." Energies 13, no. 23 (2020): 6426. http://dx.doi.org/10.3390/en13236426.
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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.
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Naomi, Anatasia, Asri Bekti Pratiwi, and Herry Suprajitno. "Grasshopper Optimizaton Algorithm (GOA) untuk Menyelesaikan Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD)." Tensor: Pure and Applied Mathematics Journal 3, no. 2 (2022): 73–84. http://dx.doi.org/10.30598/tensorvol3iss2pp73-84.
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The purpose of this paper is to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) using the Grasshopper Optimization Algorithm (GOA). Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a problem of forming routes that serve each customer, by delivering and retrieving simultaneously. The purpose of VRPSPD is to minimize the total mileage to serve all customers with the limit that each customer is served exactly once, and the vehicle load does not exceed its maximum capacity. Grasshopper Optimization Algorithm (GOA) is an algorithm inspired by nature by mimicking the living behavior of grasshopper swarms in search of food sources. GOA has several main stages, namely initialization of parameters, determination of target grasshoppers, calculating the coefficient of decline, calculating the distance between grasshoppers, and calculating the new position of the grasshoppers. Implementation of the GOA program to complete VRPSPD using the C++ programming language using 3 types of data, data with 13 customers, 22 customers, and 100 customers. Based on the results of the running program, it can be concluded that the more iterations and the number of populations, the solution obtained tends to be better.
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Y. Abdalla, Abdulkareem, and Turki Y. Abdalla. "A new modified grasshopper optimization algorithm." Bulletin of Electrical Engineering and Informatics 11, no. 5 (2022): 2756–63. http://dx.doi.org/10.11591/eei.v11i5.4083.
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The grasshopper algorithm (GOA) is a recent algorithm. It is widely used in many applications and results in a good solution. The algorithm is simple and the accuracy in very high. The GOA has some limitations due to the use of linear comfort zone parameter that causes some difficulties in balancing between the exploration and exploitation which may lead to fall in a local optimum. In this paper a modification is made to improve the operation of GOA. A nonlinear function is developed to replace the linear comfort zone parameter. The benchmark of GOA authors is used for testing the performance improvement of the suggested modified GOA compared to the basic GOA. Results indicate that the MGOA outperforms original GOA, presenting a higher accuracy, faster convergence, and stronger stability. The proposed new modified GOA performs better than the original GOA.
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Osman, Hanaa Mohammed, Rahma Saleem Alsawaf, and Asma'a Yaseen Hammo. "Survey of using grasshopper algorithm." Technium: Romanian Journal of Applied Sciences and Technology 4, no. 3 (2022): 37–44. http://dx.doi.org/10.47577/technium.v4i3.6344.
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The metaheuristic optimization algorithm is used to explain a large region solution space. One of these algorithms is a grasshopper which divides the search process into exploitation and exploration. This article focuses on research efforts directed at gaining a clear understanding of the behavior of grasshoppers and it is using optimization algorithms. It is concluded that the benefits have been effective in answering global unrestricted and restricted optimization issues, easy development, high accuracy, and obtaining a good solution. However, the disadvantages of the GOA algorithm are simple to fall into local optimum and slow convergence speed.
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Alsammarraie, Samer, and Nazar K. Hussein. "A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training." Tikrit Journal of Pure Science 25, no. 1 (2020): 118. http://dx.doi.org/10.25130/j.v25i1.944.
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The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.
 
 http://dx.doi.org/10.25130/tjps.25.2020.018
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Samer Alsammarraie and Nazar K. Hussein. "A New Hybrid Grasshopper Optimization - Backpropagation for Feedforward Neural Network Training." Tikrit Journal of Pure Science 25, no. 1 (2023): 118–27. http://dx.doi.org/10.25130/tjps.v25i1.221.
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The Grasshopper optimization algorithm showed a rapid converge in the initial phases of the global search, however while being around the global optimum, the searching process became so slow. On the contrary, the gradient descending method around achieved faster convergent speed global optimum, and the convergent accuracy was showed to be higher at the same time. As a result, the proposed hybrid algorithm combined Grasshopper optimization algorithm (GOA) along with the back-propagation (BP) algorithm, also referred to as GOA–BP algorithm, was introduced to provide training to the weights of the feed forward neural network (FNN), the proposed hybrid algorithm can utilize the strong global searching ability of the GOA, and the intense local searching ability of the Back-Propagation algorithm. The results of experiments showed that the proposed hybrid GOA–BP algorithm was better and faster in convergent speed and accuracy than the Grasshopper optimization algorithm (GOA) and BP algorithm.
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Wang, Hengfeng, Chao Liu, Huaning Wu, Bin Li, and Xu Xie. "Optimal Pattern Synthesis of Linear Array and Broadband Design of Whip Antenna Using Grasshopper Optimization Algorithm." International Journal of Antennas and Propagation 2020 (January 20, 2020): 1–14. http://dx.doi.org/10.1155/2020/5904018.
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Antenna arrays with high directivity, low side-lobe level, and null control in desired direction and whip antenna with wider bandwidth both need to be optimized to meet different needs of communication systems. A new natural heuristic algorithm simulating social behavior of grasshoppers, grasshopper optimization algorithm (GOA), is applied to electromagnetic field as a new effective technology to solve the antenna optimization problem for the first time. Its algorithm is simple and has no gradient mechanism, can effectively avoid falling into local optimum, and is suitable for single-objective and multiobjective optimization problems. GOA is used to optimize the side lobe suppression, null depth, and notch control of arbitrary linear array and then used to optimize the loading and matching network of 10-meter HF broadband whip antenna compared with other algorithms. The results show that GOA has more advantages in side-lobe suppression, null depth, and notch control of linear array than other algorithms and has better broadband optimization performance for HF whip antenna. The pattern synthesis and antenna broadband optimization based on GOA provide a new and effective method for antenna performance optimization.
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Osman-Ali, Najwan, and Junita Mohamad-Saleh. "An Adaptive Average Grasshopper Optimization Algorithm for Solving Numerical Optimization Problems." WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL 18 (May 10, 2023): 121–35. http://dx.doi.org/10.37394/23203.2023.18.13.
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The grasshopper optimization algorithm (GOA), inspired by the behavior of grasshopper swarms, has proven efficient in solving globally constrained optimization problems. However, the original GOA exhibits some shortcomings in that its original linear convergence parameter causes the exploration and exploitation processes to be unbalanced, leading to a slow convergence speed and a tendency to fall into a local optimum trap. This study proposes an adaptive average GOA (AAGOA) with a nonlinear convergence parameter that can improve optimization performance by overcoming the shortcomings of the original GOA. To evaluate the optimization capability of the proposed AAGOA, the algorithm was tested on the CEC2021 benchmark set, and its performance was compared to that of the original GOA. According to the analysis of the results, AAGOA is ranked first in the Friedman ranking test and can produce better optimization results compared to its counterparts.
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Zhou, Hanfeng, Zewei Ding, Hongxin Peng, et al. "An Improved Grasshopper Optimizer for Global Tasks." Complexity 2020 (September 23, 2020): 1–23. http://dx.doi.org/10.1155/2020/4873501.
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The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.
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Sharma, Satender, Usha Chauhan, Ruqaiya Khanam, and Krishna Kant Singh. "Digital Watermarking using Grasshopper Optimization Algorithm." Open Computer Science 11, no. 1 (2021): 330–36. http://dx.doi.org/10.1515/comp-2019-0023.
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Abstract The advancement in computer science technology has led to some serious concerns about the piracy and copyright of digital content. Digital watermarking technique is widely used for copyright protection and other similar applications. In this paper, a technique for digital watermarking based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Grasshopper Optimization Algorithm (GOA) is proposed. The method computes the DWT of the cover image to obtain the sub-components of the image. The subcomponent is converted to frequency domain using DCT. The challenge is to find the optimal scaling factor to be used for watermarking. The authors have designed a GOA based technique that finds the optimized scaling factor and the coefficient for embedding the watermark. GOA makes the watermark undetectable and is invisible in the cover image. The watermark image is embedded in the cover image using these coefficients. The extraction of watermark from the cover image is done by using inverse DCT and DWT. The proposed method is compared with the other state of the art methods. The effectiveness of the proposed method is computed using Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Image Fidelity (IF). The proposed method outperforms the other methods and can be effectively used for practical digital watermarking.
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Li, Wannian, and Jinfeng Xiao. "Research on Transformer Fault Diagnosis Model Based on Improved GOA-SVM." Journal of Physics: Conference Series 2465, no. 1 (2023): 012017. http://dx.doi.org/10.1088/1742-6596/2465/1/012017.
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Abstract A transformer fault diagnosis model based on the improved grasshopper optimization algorithm-optimized support vector machine (SVM) is proposed to improve the precision of transformer fault diagnosis and avoid the issues that the traditional grasshopper optimization algorithm (GOA) is prone to falling into local optimal solution and slow convergence. Firstly, the grasshopper population is initialized using the elite backward learning strategy to improve the initial population quality and search efficiency. Then, the Sigmoid function is introduced to improve the linear weight decreasing of the grasshopper algorithm into nonlinear weight decreasing to strike a balance between the algorithm’s capacity for both local and global exploration. Finally, the kernel function parameters and penalty coefficients of the SVM are optimized with the improved grasshopper optimization algorithm (IGOA) to establish a model based on dissolved gas analysis (DGA) in oil-based IGOA algorithm-optimized SVM for transformer fault diagnosis model and verify the effectiveness and superiority of IGOA-SVM to identify transformer fault states by comparing with PSO-SVM and GOA-SVM.
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Saxena, Akash, Shalini Shekhawat, and Rajesh Kumar. "Application and Development of Enhanced Chaotic Grasshopper Optimization Algorithms." Modelling and Simulation in Engineering 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/4945157.
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In recent years, metaheuristic algorithms have revolutionized the world with their better problem solving capacity. Any metaheuristic algorithm has two phases: exploration and exploitation. The ability of the algorithm to solve a difficult optimization problem depends upon the efficacy of these two phases. These two phases are tied with a bridging mechanism, which plays an important role. This paper presents an application of chaotic maps to improve the bridging mechanism of Grasshopper Optimisation Algorithm (GOA) by embedding 10 different maps. This experiment evolves 10 different chaotic variants of GOA, and they are named as Enhanced Chaotic Grasshopper Optimization Algorithms (ECGOAs). The performance of these variants is tested over ten shifted and biased unimodal and multimodal benchmark functions. Further, the applications of these variants have been evaluated on three-bar truss design problem and frequency-modulated sound synthesis parameter estimation problem. Results reveal that the chaotic mechanism enhances the performance of GOA. Further, the results of the Wilcoxon rank sum test also establish the efficacy of the proposed variants.
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Feng, Hangwei, Hong Ni, Ran Zhao, and Xiaoyong Zhu. "An Enhanced Grasshopper Optimization Algorithm to the Bin Packing Problem." Journal of Control Science and Engineering 2020 (March 10, 2020): 1–19. http://dx.doi.org/10.1155/2020/3894987.
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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.
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Deghbouch, Hicham, and Fatima Debbat. "Hybrid Bees Algorithm with Grasshopper Optimization Algorithm for Optimal Deployment of Wireless Sensor Networks." Inteligencia Artificial 24, no. 67 (2021): 18–35. http://dx.doi.org/10.4114/intartif.vol24iss67pp18-35.
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This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm’s accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption.
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Wang, Gui-Ling, Shu-Chuan Chu, Ai-Qing Tian, Tao Liu, and Jeng-Shyang Pan. "Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem." Entropy 24, no. 6 (2022): 777. http://dx.doi.org/10.3390/e24060777.
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The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm’s exploration capability and the solution’s quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm’s validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.
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Hazra, Sunanda, Tapas Pal, and Provas Kumar Roy. "Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm." International Journal of Swarm Intelligence Research 10, no. 1 (2019): 38–57. http://dx.doi.org/10.4018/ijsir.2019010103.
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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.
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Das, Madhusmita, Biju R. Mohan, Ram Mohana Reddy Guddeti, and Nandini Prasad. "Hybrid Bio-Optimized Algorithms for Hyperparameter Tuning in Machine Learning Models: A Software Defect Prediction Case Study." Mathematics 12, no. 16 (2024): 2521. http://dx.doi.org/10.3390/math12162521.
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Addressing real-time optimization problems becomes increasingly challenging as their complexity continues to escalate over time. So bio-optimization algorithms (BoAs) come into the picture to solve such problems due to their global search capability, adaptability, versatility, parallelism, and robustness. This article aims to perform hyperparameter tuning of machine learning (ML) models by integrating them with BoAs. Aiming to maximize the accuracy of the hybrid bio-optimized defect prediction (HBoDP) model, this research paper develops four novel hybrid BoAs named the gravitational force Lévy flight grasshopper optimization algorithm (GFLFGOA), the gravitational force Lévy flight grasshopper optimization algorithm–sparrow search algorithm (GFLFGOA-SSA), the gravitational force grasshopper optimization algorithm–sparrow search algorithm (GFGOA-SSA), and the Lévy flight grasshopper optimization algorithm–sparrow search algorithm (LFGOA-SSA). These aforementioned algorithms are proposed by integrating the good exploration capacity of the SSA with the faster convergence of the LFGOA and GFGOA. The performances of the GFLFGOA, GFLFGOA-SSA, GFGOA-SSA, and LFGOA-SSA are verified by conducting two different experiments. Firstly, the experimentation was conducted on nine benchmark functions (BFs) to assess the mean, standard deviation (SD), and convergence rate. The second experiment focuses on boosting the accuracy of the HBoDP model through the fine-tuning of the hyperparameters in the artificial neural network (ANN) and XGBOOST (XGB) models. To justify the effectiveness and performance of these hybrid novel algorithms, we compared them with four base algorithms, namely the grasshopper optimization algorithm (GOA), the sparrow search algorithm (SSA), the gravitational force grasshopper optimization algorithm (GFGOA), and the Lévy flight grasshopper optimization algorithm (LFGOA). Our findings illuminate the effectiveness of this hybrid approach in enhancing the convergence rate and accuracy. The experimental results show a faster convergence rate for BFs and improvements in software defect prediction accuracy for the NASA defect datasets by comparing them with some baseline methods.
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Aksyarafah, Adifa Yasin, and Nughthoh Arfawi Kurdhi. "Optimized Approach to Electric Vehicle Routing Problem with Time Windows Using Grasshopper Optimization Algorithm." Jambura Journal of Mathematics 7, no. 1 (2025): 101–5. https://doi.org/10.37905/jjom.v7i1.30664.
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The Electric Vehicle Routing Problem with Time Windows (EVRPTW) is a complex logistics issue that involves optimizing delivery routes for electric vehicles while adhering to strict time limits, managing limited battery capacity, and addressing recharging needs. In this research, we introduce an optimized method to tackle the EVRPTW using the Grasshopper Optimization Algorithm (GOA), a metaheuristic inspired by the swarming behavior of grasshoppers. We utilize the Solomon dataset, a recognized benchmark in logistics and vehicle routing, to assess the effectiveness of our proposed algorithm. Our focus is on minimizing the total distance traveled while ensuring timely deliveries and effectively managing battery logistics and recharging. Comparative analysis indicates that the GOA surpasses traditional methods in route efficiency, reducing travel distances, and enhancing logistical operations. This study highlights the potential of GOA as a valuable tool for overcoming the challenges associated with electric vehicle logistics, paving the way for more sustainable and efficient transportation systems.
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M., Bahy, S. Nada Adel, H. Elbanna S., and A. Morsy Shanab M. "Voltage control of switched reluctance generator using grasshopper optimization algorithm." International Journal of Power Electronics and Drive System (IJPEDS) 11, no. 1 (2020): 75–85. https://doi.org/10.11591/ijpeds.v11.i1.pp75-85.
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This paper introduces a terminal voltage control approach of a Switched Reluctance Generator (SRG) based wind turbine generating systems. The control process is employed using a closed loop stimulated by the error between the reference voltage and the generator output voltage due to load and wind speed variation. This error feeds the tuned Proportional Integral controller (PI). The tuning by conventional analytical methods of the PI controller is difficult due to substantial non-linearity. A new strategy approach for evaluating optimum PI controller parameters of voltage control of SRG using the Grasshopper Optimization Algorithm (GOA) is addressed here. This approach is a simple and effective algorithm, capable of solving numerous optimization issues. The simple algorithm ensures that the optimum PI controller parameters are optimized with great quality. The performance of the proposed GOA-PI controller is achieved by using the integral of time weighted squared error (ITSE). The effectiveness of the proposed strategy is tested with the three-phase 12/8 structure SRG. Outcomes indicate the supremacy of GOA over Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) in terms of control performance measures.
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Shareef, Asmaa, and Salah Al-Darraji. "Grasshopper optimization algorithm based path planning for autonomous mobile robot." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3551–61. http://dx.doi.org/10.11591/eei.v11i6.4098.
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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.
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Asmaa, Shareef, and Al-Darraji Salah. "Grasshopper optimization algorithm based path planning for autonomous mobile robot." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3551~3561. https://doi.org/10.11591/eei.v11i6.4098.
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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.
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Elshara, Rafa, and Aybaba Hançerlioğullari. "Parameter Estimation of PV Solar Cells and Modules using Metaheuristic Optimization Algorithm." Inspiring Technologies and Innovations 3, no. 1 (2024): 9–16. https://doi.org/10.5281/zenodo.12597777.
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Photovoltaic (PV) solar cells and modules are crucial components of renewable energy systems, necessitating accurate parameter estimation for optimal performance and efficiency. This paper proposes the utilization of the Grasshopper Optimization Algorithm (GOA) for parameter estimation in PV solar cells and modules. The proposed methodology aims to enhance the accuracy and efficiency of parameter estimation by leveraging the unique search mechanism of the GOA, which mimics the foraging behavior of grasshoppers in nature. Through iterative optimization, the GOA efficiently explores the solution space to identify optimal parameters that best fit experimental data, such as current-voltage (IV) and power-voltage (PV) characteristics. The paper provides a comprehensive overview of the parameter estimation process, detailing the formulation of the objective function to minimize the error between experimental and simulated data. Furthermore, it discusses the implementation of the GOA algorithm and its integration with mathematical models of PV solar cells and modules. To validate the effectiveness of the proposed approach, experimental data from real-world PV systems are utilized. Comparative analyses with other optimization algorithms demonstrate the superior performance of the GOA in terms of convergence speed and accuracy in parameter estimation. The results indicate that the proposed methodology offers a robust and efficient solution for parameter estimation in PV solar cells and modules, thereby facilitating the design, optimization, and maintenance of photovoltaic systems. The integration of the GOA algorithm contributes to advancing the state-of-the-art in renewable energy technologies, promoting the widespread adoption of solar power generation for sustainable development. The proposed algorithm significantly outperforms all competitors in SMD, with WOA being the closest but still 26.1% worse. While GWO performs well in DDM, it still lags behind the suggested method by 31.7%. Although achieving comparable results to COA in PV, the proposed algorithm maintains an edge with COA trailing by 4.2%.
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LV, Zhaoming, and Rong PENG. "Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment." Wuhan University Journal of Natural Sciences 27, no. 3 (2022): 240–54. http://dx.doi.org/10.1051/wujns/2022273240.
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Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven to be an effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency, especially the multi-objective ontology alignment model. The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications. In this paper, two multi-objective grasshopper optimization algorithms (MOGOA) are proposed to enhance ontology alignment. One is ε-dominance concept based GOA (EMO-GOA) and the other is fast Non-dominated Sorting based GOA (NS-MOGOA). The performance of the two methods to align the ontology is evaluated by using the benchmark dataset. The results demonstrate that the proposed EMO-GOA and NS-MOGOA improve the quality of ontology alignment and reduce the running time compared with other well-known metaheuristic and the state-of-the-art ontology alignment methods.
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Al-Zoubi, Asem S., Anas Atef Amaireh, and Nihad I. Dib. "Comparative and comprehensive study of linear antenna arrays’ synthesis." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2645. http://dx.doi.org/10.11591/ijece.v12i3.pp2645-2654.
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<span>In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), antlion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.</span>
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Asem, S. Al-Zoubi, Atef Amaireh Anas, and I. Dib Nihad. "Comparative and comprehensive study of linear antenna arrays' synthesis." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 2645–54. https://doi.org/10.11591/ijece.v12i3.pp2645-2654.
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In this paper, a comparative and comprehensive study of synthesizing linear antenna array (LAA) designs, is presented. Different desired objectives are considered in this paper; reducing the maximum sidelobe radiation pattern (i.e., pencil-beam pattern), controlling the first null beamwidth (FNBW), and imposing nulls at specific angles in some designs, which are accomplished by optimizing different array parameters (feed current amplitudes, feed current phase, and array elements positions). Three different optimization algorithms are proposed in order to achieve the wanted goals; grasshopper optimization algorithms (GOA), ant lion optimization (ALO), and a new hybrid optimization algorithm based on GOA and ALO. The obtained results show the effectiveness and robustness of the proposed algorithms to achieve the wanted targets. In most experiments, the proposed algorithms outperform other well-known optimization methods, such as; Biogeography based optimization (BBO), particle swarm optimization (PSO), firefly algorithm (FA), cuckoo search (CS) algorithm, genetic algorithm (GA), Taguchi method, self-adaptive differential evolution (SADE), modified spider monkey optimization (MSMO), symbiotic organisms search (SOS), enhanced firefly algorithm (EFA), bat flower pollination (BFP) and tabu search (TS) algorithm.
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Ali, E. S., and S. M. Abd Elazim. "Power System Stability Enhancement Using Grasshopper Optimization Approach and PSSs." WSEAS TRANSACTIONS ON POWER SYSTEMS 18 (October 3, 2023): 135–40. http://dx.doi.org/10.37394/232016.2023.18.14.
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A new meta-heuristic algorithm namely Grasshopper Optimization Approach (GOA) for Power System Stabilizer (PSS) design problem is investigated in this paper. The parameters of PSSs are optimized by GOA to minimize the time domain objective function. The performance of the designed GOA based PSSs (GOAPSS) has been has been compared with Differential Evolution (DE) based PSSs (DEPSS) and the Particle Swarm Optimization (PSO) based PSSs (PSOPSS) under various loading events. The results of the proposed GOAPSS are confirmed via eigenvalues, damping ratio, time domain analysis, and performance indices. Moreover, the robustness of the GOA in getting good damping characteristics is verified.
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Anas, A. Amaireh, I. Dib Nihad, and S. Al-Zoubi Asem. "Synthesis of new antenna arrays with arbitrary geometries based on the superformula." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6228–38. https://doi.org/10.11591/ijece.v12i6.pp6228-6238.
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The synthesis of antenna arrays with low sidelobe levels is needed to enhance the communication systems’ efficiency. In this paper, new arbitrary geometries that improve the ability of the antenna arrays to minimize the sidelobe level, are proposed. We employ the well-known superformula equation in the antenna arrays field by implementing the equation in the general array factor equation. Three metaheuristic optimization algorithms are used to synthesize the antenna arrays and their geometries; antlion optimization (ALO) algorithm, grasshopper optimization algorithm (GOA), and a new hybrid algorithm based on ALO and GOA. All the proposed algorithms are high-performance computational methods, which proved their efficiency for solving different real-world optimization problems. 15 design examples are presented and compared to prove validity with the most general standard geometry: elliptical antenna array (EAA). It is observed that the proposed geometries outperform EAA geometries by 4.5 dB and 10.9 dB in the worst and best scenarios, respectively, which proves the advantage and superiority of our approach.
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Elazab, Omnia S., Hany M. Hasanien, Ibrahim Alsaidan, Almoataz Y. Abdelaziz, and S. M. Muyeen. "Parameter Estimation of Three Diode Photovoltaic Model Using Grasshopper Optimization Algorithm." Energies 13, no. 2 (2020): 497. http://dx.doi.org/10.3390/en13020497.
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While addressing the issue of improving the performance of Photovoltaic (PV) systems, the simulation results are highly influenced by the PV model accuracy. Building the PV module mathematical model is based on its I-V characteristic, which is a highly nonlinear relationship. All the PV cells’ data sheets do not provide full information about their parameters. This leads to a nonlinear mathematical model with several unknown parameters. This paper proposes a new application of the Grasshopper Optimization Algorithm (GOA) for parameter extraction of the three-diode PV model of a PV module. Two commercial PV modules, Kyocera KC200GT and Solarex MSX-60 PV cells are utilized in examining the GOA-based PV model. The simulation results are executed under various temperatures and irradiations. The proposed PV model is evaluated by comparing its results with the experimental results of these commercial PV modules. The efficiency of the GOA-based PV model is tested by making a fair comparison among its numerical results and other optimization method-based PV models. With the GOA, a precise three-diode PV model shall be established.
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Wang, Tiantian, Long Yang, and Qiang Liu. "Beetle swarm optimization algorithm: Theory and application." Filomat 34, no. 15 (2020): 5121–37. http://dx.doi.org/10.2298/fil2015121w.
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In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau?s optimization problem, are also considered and the proposed BSO algorithm is shown to be competitive in those applications.
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Bahy, Mohamed, Adel S. Nada, Sayed H. Elbanna, and Mohamed A. M. Shanab. "Voltage control of switched reluctance generator using grasshopper optimization algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 11, no. 1 (2020): 75. http://dx.doi.org/10.11591/ijpeds.v11.i1.pp75-85.
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<p>This paper presents<strong> </strong>a terminal voltage control approach of a Switched Reluctance Generator (SRG) based wind turbine generating systems. The control process is employed using a closed loop stimulated by the error between the reference voltage and the generator output voltage due to load and wind speed variation. This error feeds the tuned Proportional Integral controller (PI).</p><p>Tuning of PI controller by conventional analysis methods is difficult by the existence of a significant non-linearity. A novel strategy method is presented here to determine optimum PI controller parameters of voltage control of SRG using Grasshopper Optimization Algorithm (GOA). This proposed approach is a simple and effective algorithm that is able to solve many optimization problems. The simplicity of algorithm provides high quality tuning of optimal PI controller parameters. The integral of time weighted squared error (ITSE) is used as the performance of the proposed GOA-PI controller. The effectiveness of the proposed strategy is tested with the three-phase 12/8 structure SRG. Outcomes indicate the supremacy of GOA over Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO) in terms of control performance measures.</p>
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Pushpat, Harendra K., and Sanjiv Kumar Jain. "Optimizing Load frequency control of multiple areas power system using HGAGOA integrating with renewable energy and SMES effect." Journal of Integrated Science and Technology 13, no. 6 (2025): 1144. https://doi.org/10.62110/sciencein.jist.2025.v13.1144.
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Load frequency control (LFC) is pivotal in maintaining grid stability, particularly with the growing integration of renewable energy sources (RES) into power systems. This paper presents a novel approach for multi-area based LFC incorporating RES, utilizing a sophisticated metaheuristic optimization technique. By integrating various energy sources, including conventional thermal units and variable RES like wind and solar, and considering multi-area coordination and forecasting models, the proposed methodology dynamically adjusts control parameters to optimize LFC performance. The results indicate that Hybrid Genetic and Grasshopper Optimization Algorithm (HGAGOA) is the most effective algorithm for minimizing the objective function in only 10 iterations, followed by Genetic Algorithm (GA) with 40 iterations and then Grasshopper Optimization Algorithm (GOA) with greater than 50 iterations. By integrating the complementary strengths of GA and GOA, HGAGOA offers enhanced convergence and improved solution quality, making it a preferred choice for optimization tasks demanding rapid and reliable convergence. Hence, the HGAGOA outperforms the GOA and GA in both objective functions, change in frequency deviation and power deviation. This framework offers a promising avenue for sustainable and reliable power system operation, with potential applications in real-time implementation and integration with advanced grid management systems for further improvements in performance and resilience.
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Paksaz, Amirmohammad, Hanieh Zareian Beinabadi, Babak Moradi, Mobina Mousapour Mamoudan, and Amir Aghsami. "Advanced Queueing and Location-Allocation Strategies for Sustainable Food Supply Chain." Logistics 8, no. 3 (2024): 91. http://dx.doi.org/10.3390/logistics8030091.
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Background: This study presents an integrated multi-product, multi-period queuing location-allocation model for a sustainable, three-level food supply chain involving farmlands, facilities, and markets. The model employs M/M/C/K queuing systems to optimize the transportation of goods, enhancing efficiency and sustainability. A mixed-integer nonlinear programming (MINLP) approach is used to identify optimal facility locations while maximizing profitability, minimizing driver waiting times, and reducing environmental impact. Methods: The grasshopper optimization algorithm (GOA), a meta-heuristic algorithm inspired by the behavior of grasshopper swarms, is utilized to solve the model on a large scale. Numerical experiments demonstrate the effectiveness of the proposed model, particularly in solving large-scale problems where traditional methods like GAMS fall short. Results: The results indicate that the proposed model, utilizing the grasshopper optimization algorithm (GOA), effectively addresses complex and large-scale food supply chain problems. Compared to GAMS, GOA achieved similar outcomes with minimal differences in key metrics such as profitability (with a gap ranging from 0.097% to 1.11%), environmental impact (0.172% to 1.83%), and waiting time (less than 0.027%). In large-scale scenarios, GOA significantly reduced processing times, ranging from 20.45 to 64.78 s. The optimization of processing facility locations within the supply chain, based on this model, led to improved balance between cost (up to $74.2 million), environmental impact (122,112 hazardous units), and waiting time (down to 11.75 h). Sensitivity analysis further demonstrated that increases in truck arrival rates and product value had a significant impact on improving supply chain performance.
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Feng, Yi, Mengru Liu, Yuqian Zhang, and Jinglin Wang. "A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem." Complexity 2020 (December 30, 2020): 1–19. http://dx.doi.org/10.1155/2020/8870783.
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Job shop scheduling problem (JSP) is one of the most difficult optimization problems in manufacturing industry, and flexible job shop scheduling problem (FJSP) is an extension of the classical JSP, which further challenges the algorithm performance. In FJSP, a machine should be selected for each process from a given set, which introduces another decision element within the job path, making FJSP be more difficult than traditional JSP. In this paper, a variant of grasshopper optimization algorithm (GOA) named dynamic opposite learning assisted GOA (DOLGOA) is proposed to solve FJSP. The recently proposed dynamic opposite learning (DOL) strategy adopts the asymmetric search space to improve the exploitation ability of the algorithm and increase the possibility of finding the global optimum. Various popular benchmarks from CEC 2014 and FJSP are used to evaluate the performance of DOLGOA. Numerical results with comparisons of other classic algorithms show that DOLGOA gets obvious improvement for solving global optimization problems and is well-performed when solving FJSP.
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Jia, Heming, Chunbo Lang, Diego Oliva, Wenlong Song, and Xiaoxu Peng. "Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation." Remote Sensing 11, no. 9 (2019): 1134. http://dx.doi.org/10.3390/rs11091134.
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An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.
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Mandal, Barun, and Provas Kumar Roy. "A Probabilistic Multi-Objective Approach for Power Flow Optimization in Hybrid Wind-Based Power Systems Using Grasshopper Optimization Algorithm." International Journal of Swarm Intelligence Research 11, no. 4 (2020): 61–86. http://dx.doi.org/10.4018/ijsir.2020100103.
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This article introduces a grasshopper optimization algorithm (GOA) to efficiently prove its superiority for solving different objectives of optimal power flow (OPF) based on a mixture thermal power plant that incorporates uncertain wind energy (WE) sources. Many practical constraints of generators, valve point effect, multiple fuels, and the various scenarios incorporating several configurations of WEs are considered for both singles along with multi-objectives for the OPF issue. Within the article, the considered method is verified on two common bus experiment systems, i.e. IEEE 30-bus as well as the IEEE 57-bus. Here, the fuel amount minimization and emission minimization are studied as the primary purposes of a GOA-based OPF problem. However, the proposed algorithm yields a reasonable conclusion about the better performance of the wind turbine. Computational results express the effectiveness of the proposed GOA approach for the secure and financially viable of the power system under various uncertainties. The comparison is tabulated with the existing algorithms to provide superior results.
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Flayyih, Kadhim Hayyawi, and Mohsen Nickray. "Energy-efficient clustering in wireless sensor networks using metaheuristic algorithms." Edelweiss Applied Science and Technology 8, no. 6 (2024): 8582–610. https://doi.org/10.55214/25768484.v8i6.3848.
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Energy management in Wireless Sensor Networks (WSNs) remains a critical challenge, particularly in clustering processes. This article compares three optimization algorithms—Grasshopper Optimization Algorithm (GOA), Bat Algorithm (BA), and Whale Optimization Algorithm (WOA)—to achieve energy-efficient clustering and extend network lifetime. Initial cluster head placement is performed using K-means clustering, and a novel cost function is introduced that considers energy consumption and node distribution, enhancing the network’s efficiency and resilience. The algorithms are evaluated across three scenarios with varying base station (BS) placements. In the simplest scenario, with the BS centrally located, GOA slightly outperforms WOA in extending network lifetime, although WOA remains competitive. BA, while energy-efficient, lags behind GOA and WOA. As complexity increases with BS placement at the edge, WOA demonstrates superior energy management, delaying node death and extending network lifetime more effectively than GOA and BA. In the most challenging scenario, where the BS is placed in a remote corner, WOA emerges as the most effective algorithm, maintaining network performance and balancing energy consumption for the longest duration. GOA, while relatively strong, shows faster network lifetime decline, particularly in later stages, whereas BA faces significant challenges, leading to quicker node failures. Overall, this study highlights the importance of efficient clustering and optimization for prolonging WSN lifetimes. WOA excels in complex scenarios, while GOA leads in simpler environments. Integrating K-means clustering with the novel cost function enhances algorithm performance, contributing to the development of resource-efficient WSNs, especially in resource-constrained settings.
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Muhammad, Nur Afida, Mohammad Faridun Naim Tajuddin, Azralmukmin Azmi, Mohd Nasrul Izzani Jamaludin, Shahrin Md Ayob, and Tole Sutikno. "Experimental study on modified GOA-MPPT for PV system under mismatch conditions." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 1 (2024): 611. http://dx.doi.org/10.11591/ijpeds.v15.i1.pp611-622.
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This paper presents a modified grasshopper optimization algorithm (GOA) tailored for optimizing the power extraction capability of a solar photovoltaic (PV) system. The algorithm`s focus is on addressing one of the issues associated with mismatch loss (MML), particularly the mismatch (MM) in solar irradiance conditions, to attain maximum output power. The core strategy of the GOA involves optimizing the duty cycles of the converter to achieve the maximum power point (MPP) for the PV system. The PV system configuration comprises three PV modules connected in series and a SEPIC converter. To facilitate efficient maximum power point tracking (MPPT), the paper proposes using the GOA as a controlling mechanism. The study employs a comparative approach, contrasting the performance of the proposed system against established algorithms, such as PSO and GWO. The results of these evaluations exhibit the superior performance of the proposed GOA when compared to other optimization techniques. The GOA exhibits exceptional MPPT tracking characteristics, characterized by rapid tracking speed, heightened efficiency, and minimal oscillations within the PV system. Consequently, the GOA effectively addresses one of the MML issues.
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Nur, Afida Muhammad, Faridun Naim Tajuddin Mohammad, Azmi Azralmukmin, Nasrul Izzani Jamaludin Mohd, Md Ayob Shahrin, and Sutikno Tole. "Experimental study on modified GOA-MPPT for PV system under mismatch conditions." Experimental study on modified GOA-MPPT for PV system under mismatch conditions 15, no. 1 (2024): 611–22. https://doi.org/10.11591/ijpeds.v15.i1.pp611-622.
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This paper presents a modified grasshopper optimization algorithm (GOA) tailored for optimizing the power extraction capability of a solar photovoltaic (PV) system. The algorithm`s focus is on addressing one of the issues associated with mismatch loss (MML), particularly the mismatch (MM) in solar irradiance conditions, to attain maximum output power. The core strategy of the GOA involves optimizing the duty cycles of the converter to achieve the maximum power point (MPP) for the PV system. The PV system configuration comprises three PV modules connected in series and a SEPIC converter. To facilitate efficient maximum power point tracking (MPPT), the paper proposes using the GOA as a controlling mechanism. The study employs a comparative approach, contrasting the performance of the proposed system against established algorithms, such as PSO and GWO. The results of these evaluations exhibit the superior performance of the proposed GOA when compared to other optimization techniques. The GOA exhibits exceptional MPPT tracking characteristics, characterized by rapid tracking speed, heightened efficiency, and minimal oscillations within the PV system. Consequently, the GOA effectively addresses one of the MML issues.
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Ebrahimabadi, Arash, and Alireza Afradi. "PREDICTION OF RATE OF PENETRATION (ROP) IN PETROLEUM DRILLING OPERATIONS USING OPTIMIZATION ALGORITHMS." Rudarsko-geološko-naftni zbornik 39, no. 3 (2024): 119–30. http://dx.doi.org/10.17794/rgn.2024.3.9.
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In drilling operations, by choosing the proper tools and also incorporating more accurate and reliable parameters, this operation can be performed in less time and cost manner. Among drilling parameters, Rate of Penetration (ROP) is viewed as the main parameter in drilling operation evaluation. Field data investigations can be considered the most fruitful approaches to evaluate drilling performance, or ROP, as well as development of predictive models although laboratory tests and experimental formulas are vastly used to identify the drilling problems. In this research, intelligent modeling was used to predict the penetration rate of drilling operations through analyses of an established comprehensive data base from drilling operations in one of Iranian oilfields, Shadegan oilfield, in which novel artificial intelligence techniques such as Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Grasshopper Optimization Algorithm (GOA) were applied. Since the database includes 400 data, these techniques were utilized due to their effectiveness on a large set of data. In this research, using drilling data compiled from Shadegan oilfield, a precise model was developed to predict the ROP. Results showed that determination coefficient (R2) and Root mean squared error (RMSE) parameters for Particle Swarm Optimization (PSO) are found to be as R2=0.977 and RMSE=0.036, for Grey Wolf Optimization (GWO) R2=0.996 and RMSE=0.014, for Grasshopper Optimization Algorithm (GOA) R2=0.999 and RMSE=0.003, respectively. Ultimately, it can be concluded that all predictive models lead to acceptable results but GOA yields more precise and realistic outcome.
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Singh, Akansha, Krishna Kant Singh, Michal Greguš, and Ivan Izonin. "CNGOD-An improved convolution neural network with grasshopper optimization for detection of COVID-19." Mathematical Biosciences and Engineering 19, no. 12 (2022): 12448–71. http://dx.doi.org/10.3934/mbe.2022584.
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<abstract><p>The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.</p></abstract>
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Matada Murigendraiah, Savitha, and Prabhugoud I. Basarkod. "Energy efficiency based RPL protocol using grasshopper optimization algorithm." Bulletin of Electrical Engineering and Informatics 13, no. 5 (2024): 3187–95. http://dx.doi.org/10.11591/eei.v13i5.7856.
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The routing protocol for low-power and lossy networks (RPL) is necessary for the internet of things (IoT) because it offers scalable, reliable, and energy-efficient routing capabilities. The trickling algorithm generates a destination-oriented directed acyclic graph (DODAG) with the broadcasting of suppression. However, broadcast suppression is insufficient when addressing network coverage and optimization problems based on uneven node distribution. Network congestion develops in large-scale IoT implementations where many devices are interconnected and congestion causes data transmission delays, decreased overall reliability, and higher latency. In this paper, the grasshopper optimization algorithm with the DODAG (GOA-DODAG) is proposed to determine optimization problems and energy-efficient reliable routing paths which include coverage-based dynamic trickling technique to construct DODAG energy-efficient without affecting the coverage of network and data routing reliability. The GOA-DODAG achieves a 98% packet delivery ratio (PDR) while consuming 0.48 mJ, which is more preferable in comparison to the existing methods like efficient-routing protocol for low-power and lossy networks (E-RPL), reliable and energy-efficient RPL (REFER), elaborated cross-layer RPL objective function to achieve energy efficiency (ELITE).
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Wijaya, Bustani Hadi, Ramadhani Kurniawan Subroto, Kuo Lung Lian, and Nanang Hariyanto. "A Maximum Power Point Tracking Method Based on a Modified Grasshopper Algorithm Combined with Incremental Conductance." Energies 13, no. 17 (2020): 4329. http://dx.doi.org/10.3390/en13174329.
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The partial shading of photovoltaic (PV) modules due to clouds or blocking objects, such as buildings or tree leaves, is a common problem for photovoltaic systems. To address this, maximum power point tracking (MPPT) is implemented to find the global maximum power point (GMPP). In this paper, a new hybrid MPPT is proposed that combines a modified grasshopper optimization algorithm (GOA) with incremental conductance (IC). In the first stage, the proposed modified GOA is implemented to find a suitable tracking area where the GMPP is located. Then the system moves to the second stage by implementing IC to get the correct GMPP. IC is a fast-performing and reliable algorithm. By combining GOA and IC, the proposed method can find the GMPP accurately with a short tracking time. Various experimental results show that the proposed method yields the highest tracking efficiency and lowest tracking time compared to some of the state-of-the-art MPPT algorithms, such as particle swarm and modified firefly optimizations.
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Akkar, Hanan, and Sameem Salman. "Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 279–93. http://dx.doi.org/10.22266/ijies2020.1231.25.
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This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.
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Poonia, Priya, and Laxmi Narayan Balai. "Evaluating the Advantages and Challenges of Mobile Ad-Hoc Networks." Journal of Computers, Mechanical and Management 2, no. 5 (2023): 01–07. http://dx.doi.org/10.57159/gadl.jcmm.2.5.230100.
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Mobile Ad-Hoc Networks (MANETs) are decentralized assemblies of mobile nodes, including smartphones, laptops, iPads, and PDAs, that operate autonomously, contrasting with conventional wireless networks. These networks dynamically adapt their topology and routing tables as nodes join or leave, ensuring a seamless data packet transmission. This article aims to provide a comprehensive overview of MANETs, elucidating their advantages, challenges, and diverse applications. Unlike traditional networks that require a centralized administrator, MANETs enable mobile nodes to exchange data packets solely through wireless links. However, the volatile topologies and limited resources challenge establishing a power-efficient and secure routing system. This study introduces a reliable routing mechanism considering network power consumption and node reputation. Utilizing a Krill Herd-based Grasshopper Optimization Algorithm (KH-GOA), in conjunction with a reputation model, the proposed system establishes a trustworthy route between the origin and destination nodes. The reputation model considers node mobility, actual capabilities, historical performance, and peer reviews. Upon evaluating these reputation metrics, the KH-GOA method is employed, amalgamating the Krill Herd (KH) and Grasshopper Optimization Algorithm (GOA) techniques. The proposed KH-GOA-based routing protocol considers multi-objective criteria like reputation, power efficiency, distance, and latency for optimal route selection.
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GÖKÇE, Harun. "Using of Grasshopper Optimization Algorithm Approach for Optimal Weight Design Problem of the Spur Gear." Mechanics 30, no. 5 (2024): 472–77. http://dx.doi.org/10.5755/j02.mech.33288.
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Gears are undoubtedly the most important parts of motion transmission. One way to increase the motion performance of the system is to reduce the weights of the gears without sacrificing the strength capability. Today, various optimization techniques that rely heavily on analytical and heuristic approaches are used for this problem. In this study, the gear optimization problem in accordance with the minimum weight was solved with Grasshopper Optimization Algorithm (GOA). Introduced in recent years, GOA is a meta-heuristic optimization technique that stands out with its successful performance in engineering applications. This approach was used in a spur gear formation case as per the minimum weight for the first time. Compared to previous studies, the results obtained in this paper show that it is possible to design a lighter gear with GOA.
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Hussien, Abdelazim G., Fatma A. Hashim, Raneem Qaddoura, Laith Abualigah, and Adrian Pop. "An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization." Processes 10, no. 11 (2022): 2254. http://dx.doi.org/10.3390/pr10112254.
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Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle algorithm (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in the sub-optimal region and have a slow convergence, especially in high-dimensional tasks problems. This paper suggests an enhanced ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as an internal operator in the updating process. ErWCA also uses a control-randomization operator. To verify this version, a comparison between EErWCA and other algorithms, namely, classical ErWCA, water cycle algorithm (WCA), butterfly optimization algorithm (BOA), bird swarm algorithm (BSA), crow search algorithm (CSA), grasshopper optimization algorithm (GOA), Harris Hawks Optimization (HHO), whale optimization algorithm (WOA), dandelion optimizer (DO) and fire hawks optimization (FHO) using IEEE CEC 2017, was performed. The experimental and analytical results show the adequate performance of the proposed algorithm.
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Amit Kumar Mittal. "Enhancing Solar Power Forecasting using Grasshopper optimization and Whale Optimization Algorithm." Journal of Electrical Systems 20, no. 3 (2024): 2054–59. http://dx.doi.org/10.52783/jes.4005.
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Solar power forecasting is essential for effectively integrating solar energy into the power system. Accurate forecasting allows for more effective power system planning, operation, and management. In this research work, we employed the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA) to choose features for solar power forecasting utilizing time series data from the OPSD dataset. The dataset contains measurements taken at 15-minute intervals, giving a wealth of data for training and verifying forecasting algorithms. The WOA is used to adjust the parameters of a forecasting model, hence increasing its accuracy and reliability. The suggested approach's performance is evaluated on a large-scale dataset, with training, validation, and test sets of 100,000, 50,000, and 51,347 data points, respectively. The results show that the WOA is effective at improving solar power forecasting accuracy, which contributes to the efficient use of renewable energy resources.
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Tripathy, Debasis, Nalin Behari Dev Choudhury, and Binod Kumar Sahu. "Grasshopper Optimization Algorithm-Based Fuzzy-2DOF-PID Controller for LFC of Interconnected System With Nonlinearities." International Journal of Social Ecology and Sustainable Development 12, no. 3 (2021): 11–29. http://dx.doi.org/10.4018/ijsesd.2021070102.
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The load frequency control (LFC) is an automation scheme employed for an interconnected power system to overcome the frequency deviation issue because of load variation in the most economical way. This work puts an earliest effort to study the LFC issue of a three-area power systems including nonlinearities using fuzzy-two degree of freedom-PID (F-2DOF-PID) controller optimized with grasshopper optimization algorithm (GOA). Initially, GOA optimized PID controllers are considered for a two area non-reheat thermal system including generation rate constraint to validate the superiority over PID controllers tuned with some recently reported optimization techniques, such as hybrid firefly algorithm-pattern search, firefly algorithm, bacteria foraging optimization algorithm, genetic algorithm, and conventional Ziegler Nichols technique. Then the work is reconsidered for the same system to verify the supremacy of F-2DOF-PID controller over other controllers such as fuzzy-PID, two degree of freedom-PID, and PID with GOA framework. Furthermore, the study is extended to a three-area system considering the effect of nonlinearities to verify effectiveness and robustness of proposed controller.
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Chew, W. T., W. V. Yong, J. S. L. Ong, J. H. Leong, and T. Sutikno. "Dynamic simulation of three-phase nine-level multilevel inverter with switching angles optimized using nature-inspired algorithm." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 1 (2021): 325. http://dx.doi.org/10.11591/ijpeds.v12.i1.pp325-333.
Full textAbstract:
This paper recommends the use of grasshopper optimization algorithm (GOA), a nature-inspired optimization algorithm, for optimizing switching-angle applied to cascaded H-bridge multilevel inverter (CHBMLI). Switching angles are selected based on the minimum value of the objective function formulated using the concept of selective harmonic minimization pulse width modulation (SHMPWM) technique. MATLAB/Simulink-PSIM dynamic co-simulation conducted on a 3-phase 9-level CHBMLI shows that the CHBMLI controlled using GOA derived switching-angle is able to respond to varying modulation index demand and synthesize an AC staircase output voltage waveform with the desired fundamental harmonic and minimized selected low-order harmonics. Compared to Newton Raphson (NR) technique, GOA is able to find optimum switching-angle solutions over a wider modulation index range. Compared to Genetic Algorithm (GA), GOA is able to find global minima with higher probability. The simulation results validate the performance of GOA for switching-angle calculation based on the concept of SHMPWM.
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W., T. Chew, V. Yong W., S. L. Ong J., H. Leong J., and Sutikno T. "Dynamic simulation of three-phase nine-level multilevel inverter with switching angles optimized using nature-inspired algorithm." International Journal of Power Electronics and Drive System (IJPEDS) 12, no. 1 (2021): 325–33. https://doi.org/10.11591/ijpeds.v12.i1.pp325-333.
Full textAbstract:
This paper recommends the use of grasshopper optimization algorithm (GOA), a nature-inspired optimization algorithm, for optimizing switchingangle applied to cascaded H-bridge multilevel inverter (CHBMLI). Switching angles are selected based on the minimum value of the objective function formulated using the concept of selective harmonic minimization pulse width modulation (SHMPWM) technique. MATLAB/Simulink-PSIM dynamic co-simulation conducted on a 3-phase 9-level CHBMLI shows that the CHBMLI controlled using GOA derived switching-angle is able to respond to varying modulation index demand and synthesize an AC staircase output voltage waveform with the desired fundamental harmonic and minimized selected low-order harmonics. Compared to Newton Raphson (NR) technique, GOA is able to find optimum switching-angle solutions over a wider modulation index range. Compared to Genetic Algorithm (GA), GOA is able to find global minima with higher probability. The simulation results validate the performance of GOA for switching-angle calculation based on the concept of SHMPWM.