Mind Evolutionary Computation Research Articles

Studying the Efficiency of Parallelization in Optimal Control of Multistage Chemical Reactions

In this paper, we investigate the problem of optimal control of complex multistage chemical reactions, which is considered a nonlinear global constrained optimization problem. This class of problems is computationally expensive due to the inclusion of multiple parameters and requires parallel computing systems and algorithms to obtain a solution within a reasonable time. However, the efficiency of parallel algorithms can differ depending on the architecture of the computing system. One available approach to deal with this is the development of specialized optimization algorithms that consider not only problem-specific features but also peculiarities of a computing system in which the algorithms are launched. In this work, we developed a novel parallel population algorithm based on the mind evolutionary computation method. This algorithm is designed for desktop girds and works in synchronous and asynchronous modes. The algorithm and its software implementation were used to solve the problem of the catalytic reforming of gasoline and to study the parallelization efficiency. Results of the numerical experiments are presented in this paper.

Prediction analysis of deflection in the construction of composite box-girder bridge with corrugated steel webs based on MEC-BP neural networks

This paper presents an efficient computational framework addressing the deflection prediction problem for prestressed concrete (PC) rigid frame bridge with corrugated steel webs (CSWs) erected by the balanced cantilever method. The framework consists of three stages of analysis: (1) obtain the datasets for establishing the prediction model, in which the input samples and output response are generated by the Latin hypercube sampling (LHS) technique and high-fidelity three-dimensional (3D) finite element (FE) model, respectively; (2) based on the datasets, an optimized neural network model that integrates with mind evolutionary computation (MEC) algorithm and back-propagation (BP) algorithm is established, then the model performance is evaluated by the statistical criteria; and (3) the well-trained MEC-BP model is used to predict the deflection for CSWs-PC bridge construction. The applicability and efficiency of the framework are demonstrated with a real CSWs-PC bridge. Results show that the proposed method achieves much better prediction performance than the conventional BP model, and can deliver an efficient prediction tendency for the deflection control of a CSWs-PC bridge during the complex construction process.

Software tools for analysis and synthesis of stochastic systems with high availability (XIV)

Paper presents the new multi-memetic modification of the Mind Evolutionary Computation (MEC) algorithm with the incorporated landscape analysis (LA) for solving global optimization in problems complex highly available systems (HAS). The proposed landscape analysis is based on the concept of Lebesgue integral and allows one to divide objective functions into three categories. Each category suggests a usage of specific hyper-heuristics for adaptive meme selection. The new algorithm and its software tools were utilized to solve an optimal control problem for the epidemic’s propagation model, based on the SIER model with pulse vaccination. Results of the numerical experiments demonstrate a significant influence of vaccination’s start time, frequency and intensity on the maximum number of infected individuals. Results of the numerical experiments demonstrate a significant influence of vaccination’s start time, frequency and intensity on the maximum number of infected individuals. The proposed algorithm helped to find and the optimal vaccination schedule in order to minimize the number of infect-ed individuals while also maintaining the volume of the utilized vaccine at the low level.

Novel metal-oxide arrester monitoring technology based on RFID sensor and mind evolutionary computation

This paper proposes an online monitoring method of metal-oxide arrester (MOA) based on passive RFID sensor tag and mind evolutionary computation (MEC). Firstly, an RFID sensor tag is designed to collect the leakage current of MOA, so as to realize the rapid fault location and equipment life cycle management. The proposed RFID sensor incorporates the sensor data into its ID information. The proposed power management block adopts a new architecture of a low-voltage DC-DC charge pump after a single-stage rectifier circuit. A method of MOA condition monitoring based on MEC is proposed. According to the measured operating voltage and leakage current, the parameters k and α which can reflect the aging condition of MOA are solved by using MEC's better optimization calculation ability, so as to monitor the MOA condition. In addition, the influence of harmonic voltage on the algorithm is analyzed through MATLAB simulation. The results show that the designed sensor tag can effectively measure the leakage current, and the proposed monitoring algorithm can accurately calculate the relevant parameters reflecting the state of MOA. Moreover, under the influence of harmonic voltage, the maximum errors of parameters k and α are 1.8% and 2.0%. Compared with the existing monitoring technology, it has the advantages of low cost, high precision and rapid fault location, which provides a new method and idea for on-line monitoring of MOA.

Assessing spatiotemporal air environment degradation and improvement represented by PM2.5 in China using two-phase hybrid model

As the largest developing country in the world, China is currently facing great pressure from poor air quality. PM2.5 is the primary pollutant causing ambient air quality problems in most cities. In the present study we developed a two-phase hybrid model, with long time scale and spatial range, that considered nonlinear, seasonal, and regional characteristics of the relationship between aerosol optical depth (AOD) and PM2.5, included meteorological parameters, and developed combinations with the ε-support vector regression (ε-SVR) and the Mind Evolutionary Computation–BP artificial neural network (MEC–BP). We used estimated and observed data to help clarify the degradation and improvement processes operating in the Chinese air environment. 2014 was an important turning point for air quality in China, as the situation changed from worsening to improving; our results are in line with the actual situation, indicating that PM2.5 pollution in China has been alleviated to a certain extent since 2013. However, during the period 2000–2017, the yearly average PM2.5 concentration in more than 60 % of China exceeded the second level criterion of the applicable national standard. Although PM2.5 pollution in China has improved to a certain extent, the air pollution situation in the country must still be regarded as severe.

Intelligent modelling to predict heat transfer coefficient of vacuum glass insulation based on thinking evolutionary neural network

Vacuum glass is widely used in many construction applications, including single-family homes, as a proven energy-saving method with outstanding heat preservation characteristics. The thermal insulation performance of vacuum glass is closely related to its heat transfer coefficient. In this study, we applied neural network methods to predict the heat transfer coefficients of vacuum glass. Using MATLAB, a neural network intelligence model was established, and the traditional back-propagation neural network (BPNN) was optimised. First, a genetic algorithm was used to reduce the dimensions of the independent variable. Then, the Mind Evolutionary Computation algorithm was used to optimise the initial weight and threshold. Using the optimised BPNN intelligence model to predict the heat transfer coefficient of vacuum glass insulation, we derived an average absolute error of 0.0076.

Adaptive Load Balancing in the Modified Mind Evolutionary Computation Algorithm

The paper presents an adaptive load balancing method for the modified parallel Mind Evolutionary Computation (MEC) algorithm. The proposed method takes into account an objective function's topology utilizing the information obtained during the landscape analysis stage as well as the information on available computational resources. The modified MEC algorithm and proposed static load balancing method are designed for loosely coupled parallel computing systems and imply a minimal number of interactions between computational nodes when solving global optimization problems. A description of the proposed method is presented in this work along with the results of computational experiments, which were carried out with a use of multi-dimensional benchmark functions of various classes. Obtained results demonstrate that an effective use of available computational resources in the proposed method helps finding a better solution comparing to the traditional parallel MEC algorithm balancing. Further development of the proposed method requires more advanced termination criteria in order to avoid excessive iterations.

Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features

To increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR) is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD), EEMD-BP and MEC-BP models based on the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). Our hybrid model is proven to be an effective approach to predict aquaculture DO.

Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method

Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid.

A Hybrid Model Equipped with the Minimum Cycle Decomposition Concept for Short-Term Forecasting of Electrical Load Time Series

Electricity load forecasting is an essential, however complicated work. Due to the influence of a large number of uncertain factors, it shows complicated nonlinear combination features. Therefore, it is difficult to improve the prediction accuracy and the tremendous breadth of applicability especially for using a single method. In order to improve the performance including accuracy and applicability of electricity load forecasting, in this paper, a concept named minimum cycle decomposition (MCD) that the raw data are grouped according to the minimum cycle was proposed for the first time. In addition, a hybrid prediction model (HMM) based on one-order difference, ensemble empirical model decomposition (EEMD), mind evolutionary computation (MEC) and wavelet neural network (WNN) was also proposed in this study. The HMM model consists of two parts. Part one, pre-processing, known as one order difference to remove the trend of subsequence and EEMD to reduce the noise, was performed by HMM model on each subset. Part two, the WNN optimized by MEC (WNN + MEC) was applied on resultant subseries. Finally, a number of different models were used as the comparative experiment to validate the effectiveness of the presented method, such as back propagation neural network (BP-1), BPNN combined MCD (BP-2), WNN combined MCD (WNNM), a HMM (DEEPLSSVM) based on one-order difference, EEMD, particle swarm optimization and least squares support vector machine and a hybrid model (DEESGRNN) based on one-order difference, EEMD, simulate anneal and generalized regression neural network. Certain evaluation measurements are taken into account to assess the performance. Experiments were carried out on QLD (Queensland) and NSW (New South Wales) electricity markets historical data, and the experimental results show that the MCD has the advantages of improving model accuracy and of generalization ability. In addition, the simulation results also suggested that the proposed hybrid model has better performance.

Investigating the Multi-memetic Mind Evolutionary Computation Algorithm Efficiency

При решении практически значимых задач глобальной оптимизации целевая функция зачастую имеет высокую размерность и вычислительную сложность, а также нетривиальный ландшафт. Исследования показывают, что зачастую одного метода оптимизации недостаточно для эффективного решения такого рода задач – необходима гибридизация нескольких методов оптимизации.

An improved gravity compensation method for high-precision free-INS based on MEC–BP–AdaBoost

In recent years, with the rapid improvement of inertial sensors (accelerometers and gyroscopes), gravity compensation has become more important for improving navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper proposes a mind evolutionary computation (MEC) back propagation (BP) AdaBoost algorithm neural-network-based gravity compensation method that estimates the gravity disturbance on the track based on measured gravity data. A MEC–BP–AdaBoost network-based gravity compensation algorithm used in the training process to establish the prediction model takes the carrier position (longitude and latitude) provided by INS as the input data and the gravity disturbance as the output data, and then compensates the obtained gravity disturbance into the INS’s error equations to restrain the position error propagation. The MEC–BP–AdaBoost algorithm can not only effectively avoid BP neural networks being trapped in local extrema, but also perfectly solve the nonlinearity between the input and output data that cannot be solved by traditional interpolation methods, such as least-square collocation (LSC) interpolation. The accuracy and feasibility of the proposed interpolation method are verified through numerical tests. A comparison of several other compensation methods applied in field experiments, including LSC interpolation and traditional BP interpolation, highlights the superior performance of the proposed method. The field experiment results show that the maximum value of the position error can reduce by 28% with the proposed gravity compensation method.

Performance investigation of mind evolutionary computation algorithm in the context of global optimization

Performance investigation of mind evolutionary computation algorithm in the context of global optimization

A MEC-BP-Adaboost neural network-based color correction algorithm for color image acquisition equipments

Color correction algorithm based on color space conversion is an essential part in color management system. Subject to the variation in conditions such as the limitations on imaging-forming principle, device performances and machining controls, the captured images usually contain certain color distortion compared to the actual images. Therefore, the paper proposes a mind evolutionary computation (MEC)-back propagation (BP)-adaboost algorithm (Adaboost) neural network-based color correction algorithm for color image collecting equipment. MEC-BP-Adaboost is utilized in training process to establish the color mapping model, with the captured samples of the color-targets under the color image acquisition equipments as input data and the standard color data as output data. MEC-BP-Adaboost neural network-based color correction algorithm not only can further the correction accuracy, but also can solve the problem that the color correction models obtained by using the neural network methods are not unique, and the difference among that color correction models is very big. Experimental results demonstrate that the proposed method achieves much better correction performance than the polynomial regression model, the conventional BP neural network, and the genetic algorithm (GA)-BP neural network.

Multi-memetic Mind Evolutionary Computation Algorithm for Loosely Coupled Systems of Desktop Computers

Multi-memetic Mind Evolutionary Computation Algorithm for Loosely Coupled Systems of Desktop Computers

Design of the Adaptive Filter Based Mind Evolutionary Computation

This paper describes the basic idea of Mind Evolutionary Computation and its advantages, the basic steps of the algorithm .The optimal adaptive filters are design based on MEC, which don’t need any priori statistical knowledge of signal and noise, and the parameters of adaptive filters can be automatically adjusted to the best in accordance with certain criteria. Simulation results show that the proposed method can quickly design effective optimal adaptive filters.

An extended mind evolutionary computation model for optimizations

The paper makes an analysis on the simulated mechanisms of mind evolutionary computation (MEC) firstly and proposes an extended computation model for MEC (EMEC). EMEC manipulates the search based on the behavior space and the information space. All operations in the behavior space are processed based on groups that symbolize the solution area, while the operations in the information space are done based on the billboards that are used to record the evolutionary information. All components of EMEC are formulated in details, including the similar-taxis operation, the cooperation operation, and a simulated-annealing-based dissimilation operation (SADO). EMEC emphasizes on the share and the guide of the information in the search, and gets a performance superior to the simple MEC. The proposed EMEC was performed on some well-known benchmark problems. The experimental results show EMEC is a robust global optimization algorithm and can alleviate the premature convergence validly.