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Review
. 2023 Sep 6;23(18):7710.
doi: 10.3390/s23187710.

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Arooj Khan  1 , Imran Shafi  1 , Sajid Gul Khawaja  1 , Isabel de la Torre Díez  2 , Miguel Angel López Flores  3   4   5 , Juan Castañedo Galvlán  3   6   7 , Imran Ashraf  8
Affiliations
Review

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Arooj Khan et al. Sensors (Basel). .

Abstract

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.

Keywords: adaptive filtering; bit error rate; particle swarm optimization; signal quality.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Flow chart of standard PSO algorithm.
Figure 2
Figure 2
Variants of particle swarm optimization.
Figure 3
Figure 3
Convergence improvements of different variants of PSO.
Figure 4
Figure 4
Ring topology PSO algorithm.
Figure 5
Figure 5
Dynamic multi-swarm PSO.
Figure 6
Figure 6
Flow chart of hybrid PSO algorithm.
Figure 7
Figure 7
Flow chart of the cooperative PSO algorithm.
See this image and copyright information in PMC

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