Flutter may lead to collapse of long-span bridges and need to be avoided, which makes the estimation of the onset flutter essential for long-span bridge design. However, the efficiency of traditional solution methods ...
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Flutter may lead to collapse of long-span bridges and need to be avoided, which makes the estimation of the onset flutter essential for long-span bridge design. However, the efficiency of traditional solution methods of predicting onset flutter is not high. In this study, it is converted into an optimization model, which is solved by a proposed advanced particle swarm optimization (APSO) with a novel inertia weight strategy and mutation mechanism. Two well-known benchmark functions are firstly employed to validate the performance of APSO. Compared with other existing PSO methods, APSO not only has high accuracy, but also has the fastest convergence speed. Then, APSO is used to find the flutter critical wind speed of bridges. A good agreement is obtained comparing predictions on the onset flutter by the proposed methods and available tests on several study cases. In addition, the value range of APSO variables is discussed in depth. It is confirmed that the proposed APSO algorithm has excellent stability, robustness and fast convergence, which is suitable for searching onset flutter of bridges.
Purpose - This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and pr...
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Purpose - This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems. Design/methodology/approach - The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators. Findings - The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem. Originality/value - The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.
Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization *** this study,a hybrid methodology that combines the Gaussian-white-noise-mutation parti...
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Model updating issues with high-dimensional and strong-nonlinear optimization processes are still unsolved by most optimization *** this study,a hybrid methodology that combines the Gaussian-white-noise-mutation particleswarmoptimization(GMPSO),back-propagation neural network(BPNN)and Latin hypercube sampling(LHS)technique is *** this approach,as a meta-heuristic algorithm with the least modification to the standard PSO,GMPSO simultaneously offers convenient programming and good performance in *** BPNN with LHS establishes the meta-models for FEM to accelerate efficiency during the updating process.A case study of the model updating of an actual bridge with no distribution but bounded parameters was carried out using this methodology with two different objective *** considers only the frequencies of the main girder and the other considers both the frequencies and vertical displacements of typical *** updating results show that the methodology is a sound approach to solve an actual complex bridge structure and offers good agreement in the frequencies and mode shapes of the updated model and test *** on the shape comparison of the main girder at the finished state with different objective functions,it is emphasized that both the dynamic and static responses should be taken into consideration during the model updating process.
A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model ...
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A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman's rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: advanced particle swarm optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively.
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