Parameter optimization of 5.5 GHz low noise amplifier using multi-objective Firefly Algorithm

Kumar, Ram; Talukdar, Fazal A.; Rajan, Abhishek; Devi, Anandini; Raja, M. Ramkumar

doi:10.1007/s00542-018-4034-8

Cited by 14 publications

(7 citation statements)

References 21 publications

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“…Finally, a replacement is carried out by elitist selection, in which the target and trial vectors are compared considering the fitness values. The individual with better fitness is taken and it survives to perform the same optimization process that follows in the next generation, as described in (7). In DE the mutation, crossover and selection process are executed at each generation until a stop criterion is met.…”

Section: De Algorithmmentioning

confidence: 99%

“…In terms of the optimal sizing of CMOS analog integrated circuits (ICs), it remains a challenge to accomplish target specifications and, during recent years, metaheuristics have shown their usefulness in this task. For instance, some recent mono-objective metaheuristics applied in the optimization of analog ICs can be found in [1][2][3][4][5] and some multi-objective metaheuristics can be found in [6][7][8][9]. Nevertheless, the design of ICs using CMOS technology, especially in the analog domain, remains a complex process due to the many design variables, constraints and trade-offs among them.…”

Section: Introductionmentioning

confidence: 99%

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On the Sizing of CMOS Operational Amplifiers by Applying Many-Objective Optimization Algorithms

2021

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In CMOS integrated circuit (IC) design, operational amplifiers are one of the most useful active devices to enhance applications in analog signal processing, signal conditioning and so on. However, due to the CMOS technology downscaling, along the very large number of design variables and their trade-offs, it results difficult to reach target specifications without the application of optimization methods. For this reason, this work shows the advantages of performing many-objective optimization and this algorithm is compared to the well-known mono- and multi-objective metaheuristics, which have demonstrated their usefulness in sizing CMOS ICs. Three CMOS operational transconductance amplifiers are the case study in this work; they were sized by applying mono-, multi- and many-objective algorithms. The well-known non-dominated sorting genetic algorithm version 3 (NSGA-III) and the many-objective metaheuristic-based on the R2 indicator (MOMBI-II) were applied to size CMOS amplifiers and their sized solutions were compared to mono- and multi-objective algorithms. The CMOS amplifiers were optimized considering five targets, associated to a figure of merit (FoM), differential gain, power consumption, common-mode rejection ratio and total silicon area. The designs were performed using UMC 180 nm CMOS technology. To show the advantage of applying many-objective optimization algorithms to size CMOS amplifiers, the amplifier with the best performance was used to design a fractional-order integrator based on OTA-C filters. A variation analysis considering the process, the voltage and temperature (PVT) and a Monte Carlo analysis were performed to verify design robustness. Finally, the OTA-based fractional-order integrator was used to design a fractional-order chaotic oscillator, showing good agreement between numerical and SPICE simulations.

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Section: De Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Sizing of CMOS Operational Amplifiers by Applying Many-Objective Optimization Algorithms

2021

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“…The firefly with the highest relative brightness is chosen with the roulette probability method. The brightness, position, and dynamic decision domain are updated and reiterated to find the next suitable firefly [59]. The iterative process of the algorithm is divided into brightness update, position update, and dynamic decision domain update [60].…”

Section: Firefly Algorithmmentioning

confidence: 99%

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

et al. 2021

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Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

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“…El Hami et al, 2010;Reddad et al, 2022;Rhouas & El Hami, 2022;Zemzami et al, 2017Zemzami et al, , 2020, in diverse fields such as engineering design (Chien et al, 2021;Granados-Rojas et al, 2021;N. El Hami et al, 2015a, 2015bKumar et al, 2020;Ranjan et al, 2022), logistics, supply chain management, finance (N. El Hami & Bouchekourte, 2016), manufacturing and production planning, telecommunications (Ammari et al, 2014;Ghallali et al, 2013), healthcare (Alrajeh et al, 2012, environmental control and protection (Bhanja et al, 2022), energy systems (Lailianfeng & ChangTing-cheng, 2021;Obiora et al, 2021), transportation and logistics, machine learning and data mining, healthcare and water resource management. Metaheuristic optimization algorithms are a class of search algorithms designed to find near-optimal solutions to complex optimization problems (Hakima et al, 2022;Reddad et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Hybrid model of the FEM method and the SAR algorithm to perform reliability-based design optimization of the solder joints in the BGA assembly

Reddad,

Zemzami,

Hami

et al. 2024

Preprint

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Metaheuristic optimization algorithms are powerful for solving complex optimization problems, almost when we lack information about the nature of their objective function. Meanwhile, the Finite Element Method (FEM) is a very powerful method for predicting and simulating the behavior of systems subjected to various stresses, whether electrical, thermal, mechanical, vibratory or magnetic. In the current study, we have proposed an optimization methodology that couples a metaheuristic algorithm - the Search and Rescue Algorithm (SAR) - with the finite element method. We then investigated the proposed methodology for optimizing the reliability of solder balls with respect to their geometry in a Ball Grid Array (BGA) assembly subjected to thermal cycling load. First, we performed a sensitivity analysis to determine the parameters influencing solder ball reliability, then we optimized the plastic deformation of solder balls, hence increasing their reliability as a function of their geometry through the proposed methodology, which results in improving the reliability of the entire electronic component.

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Parameter optimization of 5.5 GHz low noise amplifier using multi-objective Firefly Algorithm

Cited by 14 publications

References 21 publications

On the Sizing of CMOS Operational Amplifiers by Applying Many-Objective Optimization Algorithms

On the Sizing of CMOS Operational Amplifiers by Applying Many-Objective Optimization Algorithms

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Hybrid model of the FEM method and the SAR algorithm to perform reliability-based design optimization of the solder joints in the BGA assembly

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