From feature selection to continuous optimization

Metaheuristic algorithms (MAs) have seen unprecedented growth thanks to their successful applications in fields including engineering and health sciences. In this work, we investigate the use of a deep learning (DL) model as an alternative tool to do so. The proposed method, called MaNet, is motivated by the fact that most of the DL models often need to solve massive nasty optimization problems consisting of millions of parameters. Feature selection is the main adopted concepts in MaNet that helps the algorithm to skip irrelevant or partially relevant evolutionary information and uses those which contribute most to the overall performance. The introduced model is applied on several unimodal and multimodal continuous problems. The experiments indicate that MaNet is able to yield competitive results compared to one of the best hand-designed algorithms for the aforementioned problems, in terms of the solution accuracy and scalability.Comment: Accepted for EA201

Impact of loading capability on optimal location of renewable energy systems distribution networks

Copyright © 2023 The Authors. A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage

Comparison of two novel MRAS strategies for identifying parameters in permanent magnet synchronous motors

Two Model Reference Adaptive System (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on Lyapunov stability theorem and Popov stability criterion, respectively. The proposed estimators only need online detection of currents, voltages and rotor rotation speed, and are effective in the estimation of stator resistance, inductance and rotor flux-linkage simultaneously. Their performances are compared and verified through simulations and experiments. It shows that the two estimators are simple and have good robustness against parameter variation and are accurate in parameter tracking. However, the estimator based on Popov stability criterion, which can overcome the parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator in contrast to only one integral unit in the estimator based on Lyapunov stability theorem. In addition, there is no need of the expert experience which is required in designing a Lyapunov function

Feature signature prediction of a boring process using neural network modeling with confidence bounds

Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45845/1/170_2005_Article_114.pd