This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

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An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant

This paper presents a methodology for creating a soft sensor for predicting the bearing wear of electrical machines. The technique is based on a combination of Park vector methods and a classifier based on an artificial neural network (ANN-classifier). Experiments are carried out in laboratory conditions on an asynchronous motor of AIR132M4 brand. For the experiment, the inner rings of the bearing are artificially degraded. The filtered and processed data obtained from the installation are passed through the ANN-classifier. A method of providing the data into the classifier is shown. The result is a convergence of 99% and an accuracy of 98% on the test data.

A Soft Sensor for Measuring the Wear of an Induction Motor Bearing by the Park’s Vector Components of Current and Voltage

Arithmetic Optimization Algorithm (AOA) is a recently developed population-based nature-inspired optimization algorithm (NIOA). AOA is designed under the inspiration of the distribution behavior of the main arithmetic operators in mathematics and hence, it also belongs to mathematics-inspired optimization algorithm (MIOA). MIOA is a powerful subset of NIOA and AOA is a proficient member of it. AOA is published in early 2021 and got a massive recognition from research fraternity due to its superior efficacy in different optimization fields. Therefore, this study presents an up-to-date survey on AOA, its variants, and applications. 

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A Comprehensive Survey on Arithmetic Optimization Algorithm

This chapter focuses on obtaining an optimal controller parameter with faster convergence and the best solution. Hence, to obtain the effective parameters, a novel arithmetic-trigonometric optimization algorithm is developed by using the essential arithmetic operators and the basic trigonometric functions. Different combinations of the proposed technique are created with multiple combined variations from sin, cos, and tan functions. The advantage of the methods is that they are simple, easy to implement, and can fit around the engineering and optimization problems. The simulation analysis of over thirty-three different benchmark functions is carried out, followed by the experimental investigation of the real-time pressure process plant by comparing the performance with the conventional algorithm. Lastly, a summary of the chapter is provided. 

Arithmetic-Trigonometric Optimization Algorithm

This paper presents an efficient implementation of high-pass filter using the Arithmetic Optimization Algorithm. The result of this algorithm was found to be better in terms of pass-band ripple and stop-band attenuation in comparison to the Park-McClellan (PM) algorithm. Further, the hardware implementation of the proposed design in the FPGA platform have been discussed in this proposed work. 

Design of High-Pass FIR Filter using Arithmetic Optimization Algorithm and its FPGA Implementation

This paper focuses on developing an improved arithmetic optimization algorithm to achieve better convergence during exploration and exploitation phases. The proposed algorithm has been achieved by incorporating other functions like square, cube, sine, and cosine in the algorithms’ stochastic scaling coefficient. Further, a comparison between the proposed method and the conventional technique is made on various benchmark functions. It is observed from the numerical results is that the proposed algorithm with both sin and cos have performed better concerning mean, best, and standard deviation values. Moreover, on achieving the worst global minima, the proposed algorithm showed better performance for most minor functions. The results also highlight that the proposed algorithm has shown the best performance, especially for higher-dimensional systems.

An Improved Arithmetic Optimization Algorithm

This paper focuses on the development of a bearing fault detection model for induction motors using line currents. The graphical and numerical analysis of the model has been developed using the park's vector approach and envelope based on the Hilbert transform. The proposed model has been evaluated on currents measured from eight different types of induction motors. The graphical results from the Concordia pattern between d- and q-components of stator currents show that healthy bearing behaviour is circular compared to the faulty bearing's elliptical. The numerical results show that the minimum and maximum envelope of d- and q-components of stator currents is more significant than one. The sum of Kurtosis for the envelope of d- and q-components of stator currents is less than 5.0.

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Bearing Fault Detection in Induction Motors Using Line Currents

This paper focuses on developing an adaptation algorithm for hypotrochoid spiral dynamic optimization using linear adaptive spiral radius and angle by dynamically varying the value for each iteration based on the fitness function value. The adaptive algorithm will help obtain the optimal search point compared to fixed radius and angle in conventional methods. The spiral radius and angle effect show that the spiral movement with a negative radius will boost the process of escaping from converging at local optima. The statistical results on various benchmark functions show that the proposed optimization has performed better than spiral dynamic, adaptive spiral dynamic, and hypotrochoid spiral dynamic algorithms.

R Abishek

Papers

An Improved Arithmetic Optimization Algorithm

Bearing Fault Detection in Induction Motors Using Line Currents

Adaptation of Spiral Radius and Angle in Hypotrochoid Spiral Dynamic Algorithm