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Optimization of Butterworth and Bessel Filter Parameters with Improved Tree-Seed Algorithm.

Mehmet Beşkirli¹, Mustafa Servet Kiran²

¹Department of Computer Engineering, Karamanoğlu Mehmetbey University, 70200 Karaman, Türkiye.

Biomimetics (Basel, Switzerland)

|November 24, 2023

Summary

This study optimizes active filter parameters using an improved Tree Seed Algorithm (I-TSA). The I-TSA method demonstrates successful application and prediction for filter design, enhancing performance over the basic Tree Seed Algorithm.

Keywords:

Bessel filter Butterworth filter Parameter extraction optimization tree seed algorithm (TSA)

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Area of Science:

Electrical Engineering
Signal Processing
Computational Intelligence

Background:

Active filters are crucial electrical circuits for signal processing, offering gain and impedance benefits over passive filters.
Optimizing the parameters of active filters, specifically resistors and capacitors, is essential for maximizing their functionality.
Existing optimization algorithms may require enhancement for complex filter design tasks.

Purpose of the Study:

To optimize the parameters of tenth-order Butterworth and Bessel active filters.
To introduce an improved Tree Seed Algorithm (I-TSA) by incorporating opposition-based learning (OBL).
To evaluate the performance of I-TSA against the basic Tree Seed Algorithm (TSA) and other algorithms for filter design.

Main Methods:

Utilized the Tree Seed Algorithm (TSA), a nature-inspired optimization technique.
Integrated opposition-based learning (OBL) with TSA to create an enhanced version (I-TSA).
Applied I-TSA to optimize parameters for tenth-order Butterworth and Bessel filter topologies.

Main Results:

The I-TSA method successfully optimized active filter parameters, demonstrating applicability to the design problem.
Experimental results confirmed the effectiveness of I-TSA in performing accurate filter predictions.
I-TSA showed improved performance compared to the basic TSA and other tested algorithms.

Conclusions:

The proposed I-TSA is a viable and effective method for optimizing active filter parameters.
This approach offers a robust solution for enhancing the performance and functionality of active filters.
The study highlights the potential of hybrid optimization algorithms in advanced electrical engineering applications.