RETRACTED ARTICLE: Optimization algorithms, an effective tool for the design of digital filters; a review

Ravi, Renjith V.; Subramaniam, Kamalraj; Roshini, T. V.; Muthusamy, Sundar Prakash Balaji; Venkatesan, G. K. D. Prasanna

doi:10.1007/s12652-019-01431-x

Cited by 22 publications

(4 citation statements)

References 101 publications

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“…Improvement of photoelectric performance of thin film solar cells [184] Optimization of nanosecond laser processing [185] VLSI floor planning optimization regarding measures such as area, wire length and dead space between modules [186] Lifetime reliability, performance and power consumption of heterogeneous multiprocessor embedded systems [187] MO Particle Swarm Optimization Review of many applications of MO PSO in diverse areas [188] Floor planning of the VLSI circuit and layout area minimization using MO PSO [189] MO Ant Colony Optimization A 3D printed bandpass frequency-selective surface structure with desired center frequency and bandwidth [190] Analog filter design [191] Multi-criteria optimization for VLSI floor planning [192] Artificial Bee Colony Area and power optimization for logic circuit design [193] Design of digital filters [194] Artificial Immune System Spectrum management and design of 6G networks [195] Multi-objective design of an inductor for a DC-DC buck converter [196] Differential Evolution Geometry optimization of high-index dielectric nanostructures [197] Multi-objective synchronous modeling and optimal solving of an analog IC [198] Firefly Algorithm Reducing heat generation, sizing and interconnect length for VLSI floor planning [199] Secure routing for fog-based wireless sensor networks [200] Cuckoo Search Multi-objective-derived energy-efficient routing in wireless sensor networks [201] Parameter extraction of photovoltaic cell based on a multi-objective approach [202] MO Grey Wolf Optimizer Electrochemical micro-drilling in MEMS [203] Multi-objective task scheduling in cloud-fog computing [204] Besides using metaheuristic algorithms, multi-objective optimization can be implemented using machine learning techniques such as artificial neural networks (multi-layer perceptrons), convolutional neural networks and recurrent neural networks.…”

Section: Multi-objective (Mo) Genetic Algorithmmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Jakšić

Devi²,

Jakšić

et al. 2023

Biomimetics

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The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.

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Section: Multi-objective (Mo) Genetic Algorithmmentioning

confidence: 99%

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Jakšić

Devi²,

Jakšić

et al. 2023

Biomimetics

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“…In contrast to the traditional method, procedures involved in the digital FIR flter designing process could be conceived to be an optimization problem [7], alongside the intention to minimize the error function, which basically indicates an inconsistency in the flter designed from the intended response [8]. Classical methods such as the least square and gradient-based methods, optimizing L 1 and L 2 norms, can provide better passband response and high stopband attenuation, with minimal ripples [9,10].…”

Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Kadhar¹,

Rengarajan

Tamilselvi

et al. 2023

International Transactions on Electrical Energy Systems

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The design of finite impulse response (FIR) filters involves the estimation of effective filter coefficients, making the designed filter exhibit infinite stopband attenuation and have a flat-shaped passband. The few conventional filter design methods such as impulse response truncation (IRT) and windowing technique exhibit undesirable characteristics owing to the Gibbs phenomenon, thus making them unsuitable for various practical complexities. This research work employs the fuzzy logic-based diversity-controlled self-adaptive differential evolution algorithm (FLDCSaDE) for the design of FIR band stop (BS) and high pass (HP) filters. In order to validate the results of the proposed technique, various population-based evolutionary computing techniques such as the covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), self-adaptive differential evolution (SaDE), and Jaya algorithm have also been applied for determining the effective filter coefficients. The performance of the various algorithms has been analysed and compared based on the parameters such as stopband attenuation, passband attenuation, and ripples. The simulation results show that the FLDCSaDE algorithm outperforms other evolutionary algorithms having 4% and 1.5% lower ripples than the SaDE algorithm for high pass and band stop filters, respectively. Experimental results depict that the performance of the fuzzy approach causes positioning and tracking accuracy obtained to be improved by 27% and the corresponding false positive rate (FPR) is substantially reduced to 0.11 from the mean amplitude value obtained from the fuzzy approach in the frequency response. The frequency response obtained from the FLDCSaDE algorithm is close to the ideal response of the BS and HP FIR filters.

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“…The existing approaches for measurement improvement are based on digital filtering [8,9]. Digital filters are designed for noisy sensors based on the minimization of some criterion [10], and are technologically implemented by means of oversampling and averaging [11], output filtering [12], or by using integrating analog to digital converters (ADC) [13]. Note however that the optimal processing of the signal not only depends on the sensor noise characteristics but also on the relationship between stochastic dynamical system noise (i.e.…”

Section: Introductionmentioning

confidence: 99%

Optimal averaging time for improving observer accuracy of stochastic dynamical systems

Balaguer

Ibeas

2021

ISA Transactions

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RETRACTED ARTICLE: Optimization algorithms, an effective tool for the design of digital filters; a review

Cited by 22 publications

References 101 publications

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Optimal averaging time for improving observer accuracy of stochastic dynamical systems

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