Test scheduling for system on chip using modified firefly and modified ABC algorithms

Chandrasekaran, Gokul; Sakthivel, P.; Karthikeyan, P.

doi:10.1007/s42452-019-1116-x

Cited by 62 publications

(18 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RMSE represents the existing deviation within the experimental and predicted values. MAPE estimates the error and the ratio of the error regarding the experimental values [37,38]. NSE is used to evaluate the predictive capability of the model.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN

et al. 2021

View full text Add to dashboard Cite

Fly ash, as a supplemental pozzolanic material, reduces concrete’s adverse environmental footprint by decreasing the emission of carbon dioxide (CO2) during the cement manufacturing process. Fly ash, which is a waste material, can enhance both the mechanical characteristics and durability of concrete, and has the capability to play an important role in sustainable design. Considering the widespread interest in applying Fly ash, and despite research studies, the level of replacement is still unclear. In this paper, a novel method using artificial neural networks (ANN) is presented to predict concrete’s mechanical characteristics by adding Fly ash. In this regard, a host of available experimental data, such as the properties of Fly ash, along with concrete additives, was fed into an ANN model. Concrete samples’ tensile and compressive strengths, in addition to their modulus of elasticity, were defined as outputs. It was observed that the predicted outcomes agreed well with the experimental results. To further enhance the research outcomes, simple but practical equations are presented to assess the effect of using Fly ash on concrete’s mechanical characteristics.

show abstract

Section: Resultsmentioning

confidence: 99%

Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Other studies [23,24,28,33,34] present hybrid metaheuristic algorithms for VLSI optimization. However, they require significant computational resources, compared with the proposed algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…In [34], the ACO, MACO, ABC, Modified ABC, Firefly, and Modified Firefly test-scheduling algorithms were tested on two SoC benchmark circuits. When compared with the ACO, Modified ACO, ABC, Firefly, and Modified Firefly algorithms, the Modified ABC algorithm's testing time was faster by 82%, 69%, 25%, 43%, and 48% for the d695 SoC, and 80%, 73%, 20%, 41%, and 47% for the p22810 SoC, respectively.…”

Section: Genetic Algorithms For Vlsi Placementmentioning

confidence: 99%

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

et al. 2020

View full text Add to dashboard Cite

This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies.The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N 2 ).

show abstract

“…Gradient-based optimization algorithms are popular, but they have the problem of convergence at local minimum for multimodal error surfaces [10]. Attempts to solve the problem of local minima and achieve global optimum solution has led many researchers to introduce the use of global optimization techniques for adaptive filter optimization such as Genetic Algorithm (GA) [11], Simulated Annealing (SA) [12], Tabu Search (TS) [13], Differential Evolution (DE) [14], Particle Swarm Optimization (PSO) [15], Ant-colony (ACO) [16], Artificial intelligence [17] Modified firefly and modified ABC algorithms [18].…”

Section: Introductionmentioning

confidence: 99%

Fetal Electrocardiogram Extraction using Moth Flame Optimization (MFO)-Based Adaptive Filter

Jibia¹,

Jibia²

2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

Effective Fetal Electrocardiogram (FECG) Extraction provides medical workers with precise knowledge for monitoring fetal health condition during gestational age. However, Fetal ECG Extraction still remains a challenge as the signal is weak and contaminated with noises of different kinds, more significantly maternal ECG. In this work, a new Moth Flame optimization algorithm (MFO)-based adaptive filter is proposed for the extraction of FECG signal. A noninvasive two-point method is used to record thoracic and abdominal ECG signals from the mother's body. The abdominal ECG (AECG) signal is made up of fetal heart signal, the distorted maternal heart signal and noise. The thoracic signal contains the undistorted maternal heart signal. The two signals are applied to an adaptive filter whose coefficients are optimally determined by the conventional least means square (LMS) algorithm and MFO. Simulation results using both synthetic signals and the real data from Physionet data base developed by MIT-BIH show the superiority of the new approach over conventional methods. The performance has been proven by observation of the quality of the extracted wave forms and quantitatively by computing the Signal to Noise ratio (SNR) which was 10.28 for proposed algorithm as compared to 0.1028 for the connectional LMS and mean square error (MSE) which was 0.0215 for the proposed algorithm as compared to 0.0275 for the convectional LMS. The results indicate that the new approach is suitable for Fetal Electrocardiogram extraction from AECG.

show abstract

Test scheduling for system on chip using modified firefly and modified ABC algorithms

Cited by 62 publications

References 24 publications

Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN

Predicting the Effect of Fly Ash on Concrete’s Mechanical Properties by ANN

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

Fetal Electrocardiogram Extraction using Moth Flame Optimization (MFO)-Based Adaptive Filter

Contact Info

Product

Resources

About