This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of ...
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This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopperoptimizationalgorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopperoptimizationalgorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopperoptimizationalgorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority.
This paper proposes a novel hybrid model by integrating data pre-processing, feature extraction (FX), and forecasting & optimization modules to improve the accuracy and convergence rate. Where, the radial basis ke...
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ISBN:
(纸本)9781665453554
This paper proposes a novel hybrid model by integrating data pre-processing, feature extraction (FX), and forecasting & optimization modules to improve the accuracy and convergence rate. Where, the radial basis kernel-principal component analysis (RB-KPCA) based FX algorithm eliminates the redundant and irrelevant features to ensure the high computational efficiency. While, the modified grasshopper optimization algorithm (mGOA) optimizes the appropriate control parameters of the conditionally restricted Boltzmann machine (CRBM) to avoid the solution trapping into the local optimum and returns the results with improved accuracy. Furthermore, since the accuracy and convergence rate are two contradictory objectives, which are difficult to achieve, simultaneously. However, the the proposed FX-CRBM-mGOA forecasting model is designed in such a way to simultaneously achieve these relatively independent objectives. To evaluate the performance in terms of MAPE, accuracy and convergence rate, the proposed model is implemented on the publically available dataset of the Dayton power grid, USA. The results show that the MAPE of the proposed model is 0.4525%, the information-based artificial neural network (MIANN) is 2.4202%, long short-term memory (LSTM) is 3.231%, ANN-based accurate and fast converging (AFC-ANN) is 2.452%, and Bi-Level is 2.123%. The devised framework outperforms MI-ANN by 3.2%, Bi-level by 2.3%, and AFC-ANN by 3.1% in terms of forecast accuracy. Furthermore, the average execution time of the proposed model is 49s, the AFC-ANN model is 69s, the Bi-level model is 52s, MI-ANN model is 78s, and LSTM model is 54s.
In this study, to improve the algorithmic performance of standard grasshopperoptimizationalgorithm (GOA) that mimics the foraging characterizations of grasshoppers as in swarms, four modified versions of which are p...
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In this study, to improve the algorithmic performance of standard grasshopperoptimizationalgorithm (GOA) that mimics the foraging characterizations of grasshoppers as in swarms, four modified versions of which are proposed to solve large-scale real-size complex both truss and frame type steel structures. All proposed GOA encoded are supplied reciprocatively data transfer with structural analysis software (SAP2000) to easily get the structural responses via open application programming interface functions. Initially, the algorithmic performances of standard GOA and its proposed modified versions are evaluated on two small-size benchmark engineering design problems, namely pressure vessel and grain train design problems. And then, a 160-bar space steel pyramid, a 693-bar space braced steel barrel vault, and a 455-member spatial braced steel frame are considered as large-scale real-size structural design problems. These structures are optimally designed to reach the best feasible economic structures with minimum design weights while satisfying the structural behavior limitations such as displacement, drift, strength, and stability that are taken from specifications of the American Institute of Steel Construction-Load and Resistance Factor Design. Consequently, the performances of all proposed GO algorithms in finding the optimum designs of large-scale real-size steel structures are compared and evaluated in detail.
In robotic control systems, autonomous car driving is considered a complicated task. The conventional modular techniques necessitate precise localization, planning, and mapping procedures to provide safe driving. Thes...
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In robotic control systems, autonomous car driving is considered a complicated task. The conventional modular techniques necessitate precise localization, planning, and mapping procedures to provide safe driving. These requirements make autonomous car driving tasks computationally ineffective and sensitive to ecological changes. In recent times, the emergence of deep learning-based approaches in autonomous car driving systems provided promising solutions for complex vision task interpretation, localization, and environmental perception. But, they create distribution mismatch issues and require huge perilous interaction information to offer safe driving with the prediction of future collisions. Therefore, to overcome this issue, a novel modified Kernel Support vector Convolutional based Hybrid grasshopper Harris hawk (MKSC-HGH) approach is proposed. The proposed MKSC-HGH approach efficiently forecasts the uncertainties of agile autonomous car driving and guides proper navigation details of the track to avoid the possibility of collision with on-road obstacles and fences. This system inputs camera images captured through sensors, car speed, and manual driving data to analyze and predict safety control and probable collision. The prominent feature representing the ability of modified Convolutional Neural Network (MCNN) and complex boundary encoding process of Kernel Support Vector Machine (KSVM) with hyperparameter tuning operation of modified grasshopper optimization algorithm based Improved Harris Hawk optimization (MGOA-IHHO) algorithm made proposed system applicable to predict future states (ie. visual prediction, speed prediction, and collision prediction) more accurately. The efficiency of the proposed MKSC-HGH approach is investigated using evaluation measures namely completion time, average speed, top speed, and laps time. The simulation outcome shows the superiority of the proposed MKSC-HGH approach in predicting agile autonomous car driving over state of art
Many day-to-day operation decisions in a smart city need short term load forecasting (STLF) of its customers. STLF is a challenging task because the forecasting accuracy is affected by external factors whose relations...
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Many day-to-day operation decisions in a smart city need short term load forecasting (STLF) of its customers. STLF is a challenging task because the forecasting accuracy is affected by external factors whose relationships are usually complex and nonlinear. In this paper, a novel hybrid forecasting algorithm is proposed. The proposed hybrid forecasting method is based on locally weighted support vector regression (LWSVR) and the modified grasshopper optimization algorithm (MGOA). Obtaining the appropriate values of LWSVR parameters is vital to achieving satisfactory forecasting accuracy. Therefore, the MGOA is proposed in this paper to optimally select the LWSVR's parameters. The proposed MGOA can be derived by presenting two modifications on the conventional GOA in which the chaotic initialization and the sigmoid decreasing criterion are employed to treat the drawbacks of the conventional GOA. Then the hybrid LWSVR-MGOA method is used to solve the STLF problem. The performance of the proposed LWSVR-MGOA method is assessed using six different real-world datasets. The results reveal that the proposed forecasting method gives a much better forecasting performance in comparison with some published forecasting methods in all cases.
The modified grasshopper optimization algorithm which identifies Parkinson disease symptoms at an early (premature) stage was proposed. Parkinson disease, type of movement ailment, could be life-threatening if not tre...
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The modified grasshopper optimization algorithm which identifies Parkinson disease symptoms at an early (premature) stage was proposed. Parkinson disease, type of movement ailment, could be life-threatening if not treated at premature stage. Therefore, diagnosis of Parkinson disease became essential in early stages so that all the symptoms could be controlled by giving required medication to the patient. Hence ensuring the patient longevity. As part of this research work, a novel model modified grasshopper optimization algorithm was introduced which was based on the traditional grasshopperoptimizationalgorithm and search strategy for feature selection. grasshopperoptimizationalgorithm was relatively a novel heuristic optimization swarm intelligence algorithm which was stimulated by grasshoppers searching for food. This population-based method has capability to provide solution for real-life problems in undefined search space. It mimics grasshopper swarm's behaviour and their social interaction. Popular algorithms like Random Forest, Decision Tree and k-Nearest Neighbour classifier were used in judgement on shortlisted aka selected features. Different datasets of handwriting (meander and spiral), speech and voice were used for evaluating the presented model. The proposed algorithm was effective in Parkinson disease identification having accuracy (computed) of 95.37%, 99.47% detection rate and 15.78% false alarm rate. This helps larger cause of patient in receiving treatment in pre-mature stage. The presented bio-inspired algorithm was adequately steady and has ability to identify the optimal feature set. Finally results obtained from the assessment of introduced modified grasshopper optimization algorithm on these data sets were evaluated and contrasted with respect to outcome of modified Grey Wolf Optimizer and Optimized Cuttlefish algorithm. The experiment's outcome revealed that the presented modified grasshopper optimization algorithm assists in reducing the
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