The density functional tight-binding (DFTB) theory and density functional theory (DFT) are separately combined with the modified basin hopping (MBH) method into a two-stage optimization algorithm that is then applied ...
详细信息
The density functional tight-binding (DFTB) theory and density functional theory (DFT) are separately combined with the modified basin hopping (MBH) method into a two-stage optimization algorithm that is then applied in sequence to find the lowest-energy structures of Au clusters. In the first-stage, the DFTB/MBH is conducted mainly to provide a speedy and yet semi-quantitatively reliable searching of the lowest-energy structures so as to pave the way for the second-stage DFT/MBH for more refined and accurate sorting of them at the DFT level. To ensure high efficiency in searching the minimized energy, we pay more attention to the DFTB theory in particular the repulsive part energy and examine the quality of the DFTB parameters fitted to different sets of reference structures. This parametrization study is physically relevant since the cluster's structure is basically determined, by and large, by the repulsive potential. To appreciate the nicety of the present method, we apply the latter and two other existing sets of DFTB parameters to perform the first-stage DFTB/MBH but, separately, we continue the second-stage optimization all by the same DFT/MBH method. We found that the lowest-energy geometries of gold clusters obtained are independent of the use of the DFTB theory to calculate the energy function in the first-stage minimization. Only the efficacy of executing the second-stage DFT/MBH which is generally time-consuming differs, however. In general, for duly-fitted DFTB parameters less computer time is required at the second-stage operation. On comparing further our calculated optimized Au structures with other theoretical calculations and existing experimental clusters, the very good agreement among them clearly explains that the present hybridized scheme has greater potential to efficiently perform with less or within affordable computing time the high-level OFT calculation for medium to larger sized clusters. (C) 2018 Elsevier B.V. All rights reserved.
In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, this study proposes a novel BBO algorithm, namely an efficient and merged biogeography-based optimization (EMBBO)...
详细信息
In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, this study proposes a novel BBO algorithm, namely an efficient and merged biogeography-based optimization (EMBBO) algorithm. Firstly, BBO's mutation operator is got rid of. Then, a differential mutation operator and a sharing operator are merged into BBO's migration operator to obtain an improved migration operator. In the improved migration operator, the emigration habitats are selected by a new example learning approach. The above improvements can enhance the optimization performance and reduce the computation complexity. Thirdly, a new single-dimensional and all-dimensional alternating strategy is combined with the improved migration operator to balance exploration and exploitation and reduce more computation complexity. Fourthly, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima. Finally, the greedy selection method is used instead of the elitist strategy to avoid setting the elitist parameter and to get rid of one sorting step. We make a large number of experiments on a set of classic benchmark functions and CEC2017 test set and apply EMBBO to clustering optimization. Experiment results verify that EMBBO can obtain the highest optimization efficiency compared with quite a few state-of-the-art algorithms.
Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in t...
详细信息
Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. (C) 2019 Elsevier B.V. All rights reserved.
The practical application of 3D inversion of gravity data requires a lot of computation time and storage *** solve this problem,we present an integrated optimization algorithm with the following components:(1)targetin...
详细信息
The practical application of 3D inversion of gravity data requires a lot of computation time and storage *** solve this problem,we present an integrated optimization algorithm with the following components:(1)targeting high accuracy in the space domain and fast computation in the wavenumber domain,we design a fast 3D forward algorithm with high precision;and(2)taking advantage of the symmetry of the inversion matrix,the main calculation in gravity conjugate gradient inversion is decomposed into two forward calculations,thus optimizing the computational efficiency of 3D gravity *** verify the calculation accuracy and efficiency of the optimization algorithm by testing various grid-number models through numerical simulation experiments.
Fractional-order calculus can obtain better results than the integer-order in control theory, so it has become a research hotspot in recent years. However, the structure of the irrational fractional-order system is co...
详细信息
Fractional-order calculus can obtain better results than the integer-order in control theory, so it has become a research hotspot in recent years. However, the structure of the irrational fractional-order system is complex, so its theoretical analysis and controller design are more difficult. In this paper, a method based on convolution integral is proposed to obtain the frequency domain response of the irrational model. Combined with the optimization algorithm, the model parameters are identified. Moreover, the rationalization of the irrational model is realized, which facilitates the analysis and application design of this kind models. Finally, two examples are given to illustrate the effectiveness and feasibility of the method by identifying parameters and rationalization.
Recommender system (RS) is an emerging technique in information retrieval to handle a large amount of online data effectively. It provides recommendation to the online user in order to achieve their correct decisions ...
详细信息
Recommender system (RS) is an emerging technique in information retrieval to handle a large amount of online data effectively. It provides recommendation to the online user in order to achieve their correct decisions on items/services quickly and easily. Collaborative filtering (CF) is one of the key approaches for RS that generates recommendation to the online user based on the rating similarity with other users. Unsupervised clustering is a class of model-based CF, which is more preferable because it provides the simple and effective recommendation. This class of CF suffers by higher error rate and takes more iterations for convergence. This study proposes a modified fuzzy c-means clustering approach to eliminate these issues. A novel modified cuckoo search (MCS) algorithm is proposed to optimize the data points in each cluster that provides an effective recommendation. The performance of proposed RS is measured by conducting experimental analysis on benchmark MovieLens dataset. To show the effectiveness of proposed MCS algorithm, the results are compared with popular optimization algorithms, namely particle swarm optimization and cuckoo search, using benchmark optimization functions.
By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic ...
详细信息
By identifying the parameters of electronic circuit, parametric fault diagnosis of power electronic circuits can be realized. Many intelligent optimization algorithms are used to identify the parameters of electronic circuit, but most of them have the defects of slow convergence rate and easy to fall into local minimum. Moth flame optimization algorithm is a novel swarm intelligence bionic algorithm based on the intelligence behavior of moth positioning, which also has the above drawbacks. In order to improve the performance of algorithm, when updating the moth position, moth firstly moves in a straight line to the optimal position, then Levy flight is added. The improved algorithm improves the global optimization ability and accelerates the convergence speed. The improved moth flame optimization algorithm is applied for the parameter identification of single-phase inverter. The identification result is compared with the results of the other optimization techniques. The effectiveness and superiority of the improved algorithm are verified.
Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in mon...
详细信息
Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974-2013) and partly for testing the models (2014-2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process. (C) 20
This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of ...
详细信息
This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo optimization algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies. (C) 2021 The Author(s). Published by Elsevier B.V.
An improved phase field method by using statistical learning theory based optimization algorithm is developed for solving the phase field equations through building simple relationships between the key phase field var...
详细信息
An improved phase field method by using statistical learning theory based optimization algorithm is developed for solving the phase field equations through building simple relationships between the key phase field variables and the phase evolution driving force, and using statistical analysis of mass computed data during phase field simulation. Phase field simulation results of growth of R phase and the B2-R phase transformation in a Ni-rich Ni50.5Ti49.5 alloy by using the proposed statistical strategy algorithm are compared with that using the conventional numerical algorithm, which demonstrates that with coupling the statistical learning theory, i.e., by means of the optimization algorithm, the credible simulated microstructure is obtained while maintaining high accuracy, and meanwhile the computational time has been significantly reduced.
暂无评论