Aneela Zameer

A new numerical approach to solve Thomas–Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programming

In this study, a novel bio-inspired computing approach is developed to analyze the dynamics of nonlinear singular Thomas–Fermi equation (TFE) arising in potential and charge density models of an atom by exploiting the strength of finite difference scheme (FDS) for discretization and optimization through genetic algorithms (GAs) hybrid with sequential quadratic programming. The FDS procedures are used to transform the TFE differential equations into a system of nonlinear equations. A fitness function is constructed based on the residual error of constituent equations in the mean square sense and is formulated as the minimization problem. Optimization of parameters for the system is carried out with GAs, used as a tool for viable global search integrated with SQP algorithm for rapid refinement of the results. The design scheme is applied to solve TFE for five different scenarios by taking various step sizes and different input intervals. Comparison of the proposed results with the state of the art numerical and analytical solutions reveals that the worth of our scheme in terms of accuracy and convergence. The reliability and effectiveness of the proposed scheme are validated through consistently getting optimal values of statistical performance indices calculated for a sufficiently large number of independent runs to establish its significance.

Wind power prediction using deep neural network based meta regression and transfer learning

Abstract An innovative short term wind power prediction system is proposed which exploits the learning ability of deep neural network based ensemble technique and the concept of transfer learning. In the proposed DNN-MRT scheme, deep auto-encoders act as base-regressors, whereas Deep Belief Network is used as a meta-regressor. Employing the concept of ensemble learning facilitates robust and collective decision on test data, whereas deep base and meta-regressors ultimately enhance the performance of the proposed DNN-MRT approach. The concept of transfer learning not only saves time required during training of a base-regressor on each individual wind farm dataset from scratch but also stipulates good weight initialization points for each of the wind farm for training. The effectiveness of the proposed, DNN-MRT technique is expressed by comparing statistical performance measures in terms of root mean squared error (RMSE), mean absolute error (MAE), and standard deviation error (SDE) with other existing techniques.

Memetic computing through bio-inspired heuristics integration with sequential quadratic programming for nonlinear systems arising in different physical models.

BackgroundIn this study, bio-inspired computing is exploited for solving system of nonlinear equations using variants of genetic algorithms (GAs) as a tool for global search method hybrid with sequential quadratic programming (SQP) for efficient local search. The fitness function is constructed by defining the error function for systems of nonlinear equations in mean square sense. The design parameters of mathematical models are trained by exploiting the competency of GAs and refinement are carried out by viable SQP algorithm.ResultsTwelve versions of the memetic approach GA-SQP are designed by taking a different set of reproduction routines in the optimization process. Performance of proposed variants is evaluated on six numerical problems comprising of system of nonlinear equations arising in the interval arithmetic benchmark model, kinematics, neurophysiology, combustion and chemical equilibrium. Comparative studies of the proposed results in terms of accuracy, convergence and complexity are performed with the help of statistical performance indices to establish the worth of the schemes.ConclusionsAccuracy and convergence of the memetic computing GA-SQP is found better in each case of the simulation study and effectiveness of the scheme is further established through results of statistics based on different performance indices for accuracy and complexity.

Machine Learning based short term wind power prediction using a hybrid learning model

Depletion of conventional resources has led to the exploration of renewable energy resources. In this regard, wind power is taking significant importance, worldwide. However, to acquire consistent power generation from wind, the expected wind power is required in advance. Consequently, various prediction models have been reported for wind power prediction. However, we observe that Support Vector Regression (SVR), and specially, a hybrid learning model based on SVR offer better performance and generalization compared to multiple linear regression (MLR) and is thus quite suitable for the development of short-term wind power prediction system. To this end, a new methodology ML-STWP namely Machine Learning based Short Term Wind Power Prediction is proposed for short-term wind power prediction. This approach utilizes a combination of machine learning (ML) techniques for feature selection and regression. The proposed methodology is thus a hybrid ML model, which makes use of feature selection through irrelevancy and redundancy filters, and then employs SVR for auxiliary prediction. Finally, the wind power is predicted using enhanced particle swarm optimization and a hybrid neural network.The wind power dataset on which the model is tuned and tested consists of real-time daily values of wind speed, relative humidity, temperature, and wind power. The obtained results demonstrate that the proposed prediction model performs better as compared to the existing methods and demonstrates the efficacy of the proposed intelligent system in accurately predicting wind power on daily basis.

Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem

In the present study, bio-inspired computational heuristics are exploited for finding the solution of economic load dispatch (ELD) problem with valve point loading effect using variants of genetic algorithm (GA) hybrid with sequential quadratic programming (SQP) and interior-point algorithms (IPAs). Variants of GAs are constructed using different sets of routines for its fundamental operators in order to explore the entire search space for global optimum solutions while SQP and IPA are integrated with GAs for rapid local convergence. Nine variants of each design scheme based on GAs, GA-SQP and GA-IPAs are applied on three different ELD problems of thermal power plant systems. Comparative studies of the proposed schemes are performed through the results of statistical performance indices in order to establish the worth and effectiveness in terms of accuracy, convergence and complexity measures.

Biologically inspired computing framework for solving two-point boundary value problems using differential evolution

In the present study, a design of biologically inspired computing framework is presented for solving second-order two-point boundary value problems (BVPs) by differential evolution (DE) algorithm employing finite difference-based cost function. The DE has been implemented to minimize the combined residue from all nodes in a least square sense. The proposed methodology has been evaluated using five numerical examples in linear and nonlinear regime of BVPs in order to demonstrate the process and check the efficacy of the implementation. The assessment and validation of the DE algorithm have been carried out by comparing the DE-computed results with exact solution as well as with the corresponding data obtained using continuous genetic algorithms. These benchmark comparisons clearly establish DE as a competitive solver in this domain in terms of computational competence and precision.

Intelligent computing to analyze the dynamics of Magnetohydrodynamic flow over stretchable rotating disk model

Abstract In this study a novel application of neurocomputing technique is presented for nonlinear fluid mechanics problem arising in the model of the flow over stretchable rotating disk in the presence of strong magnetic field. The scheme comprises of the power of effective modelling of neural networks supported with integrated optimization strength of genetic algorithm and interior-point method. The governing partial differential equation of the system is converted to nonlinear systems of simultaneous ordinary differential equations by incorporating the similarity variables. Neural network based approximate differential equation models are formulated for the transformed system that are used to construct the merit function in mean squared error sense. The networks are trained initially by genetic algorithm for the global search and rapid local refinements is attained through efficient interior point method. The given scheme is applied for dynamical analysis of the system model in terms of radial, tangential, axial velocities and heat effects by varying magnetic interaction parameters, unsteadiness factors, disk stretchable magnitudes, and Prandtl numbers. The statistical performance indices based on error from standard numerical solutions are used to validate the correctness, consistency, robustness and stability of the proposed stochastic solver.

Nature-inspired computational intelligence integration with Nelder–Mead method to solve nonlinear benchmark models

Abstract In the present study, nature-inspired computing technique has been designed for the solution of nonlinear systems by exploiting the strength of particle swarm optimization (PSO) hybrid with Nelder–Mead method (NMM). Fitness function based on least square approximation theory is developed for the systems, while optimization of the design variables is performed with PSO, an efficient global search method, refined with NMM for rapid local convergence. Sixteen variants of the proposed hybrid scheme PSO-NMM have been evaluated on five benchmark nonlinear systems, namely interval arithmetic benchmark model, kinematic application model, neurophysiology problem, combustion model and chemical equilibrium system. Reliability and effectiveness of the proposed solver have been validated after comparison with the results of statistical analysis based on massive data generated for sufficiently large number of independent executions.

Nature-inspired heuristic paradigms for parameter estimation of control autoregressive moving average systems

Aim of this research is to explore the strength of evolutionary and swarm intelligence techniques for parameter identification of control autoregressive moving average (CARMA) systems. The fitness function for CARMA system identification problem is formulated through error function created in mean square sense, and learning of unknown parameters of the system model is carried out with an effective global search techniques based on genetic algorithms and particle swarm optimization algorithm. Comparative study of the design methodology is conducted from actual parameters of the systems for different values of noise variance and degree of freedom in CARMA identification model. The correctness of the proposed scheme is validated through the results of various performance measures based on mean absolute error, mean weight deviation, variance account for and Theil’s inequality coefficient, and their global variants for sufficiently large number of independent runs.

Wind Power Prediction Using Genetic Programming Based Ensemble of Artificial Neural Networks (GPeANN)

Over the past couple of years, the share of wind power in electrical power system has increased considerably. Because of the irregular characteristics of wind, the power generated by the wind turbines fluctuates continuously. The unstable nature of the wind power thus poses a serious challenge in power distribution systems. For reliable power distribution, wind power prediction system has become an essential component in power distribution systems. In this Paper, a wind power forecasting strategy composed of Artificial Neural Networks (ANN) and Genetic Programming (GP) is proposed. Five neural networks each having different structure and different learning algorithm were used as base regressors. Then the prediction of these neural networks along with the original data is used as input for GP based ensemble predictor. The proposed wind power forecasting strategy is applied to the data from five wind farms located in same region of Europe. Numerical results and comparison with existing wind power forecasting strategies demonstrates the efficiency of the proposed strategy.