bound optimization
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TecnoLógicas ◽  
2021 ◽  
Vol 24 (51) ◽  
pp. e1866
Author(s):  
Anderson S. Quintero ◽  
Ricardo E. Gutiérrez-Carvajal

To show the potential of non-commensurable fractional-order dynamical systems in modeling epidemiological phenomena, we will adjust the parameters of a fractional generalization of the SIR model to describe the population distributions generated by SARS-CoV-2 in France and Colombia. Despite the completely different contexts of both countries, we will see how the system presented here manages to adequately model them thanks to the flexibility provided by the fractional-order differential equations. The data for Colombia were obtained from the records published by the Colombian Ministry of Information Technology and Communications from March 24 to July 10, 2020. Those for France were taken from the information published by the Ministry of Solidarity and Health from May 1 to September 6, 2020. As for the methodology implemented in this study, we conducted an exploratory analysis focused on solving the fractional SIR model by means of the fractional transformation method. In addition, the model parameters were adjusted using a sophisticated optimization method known as the Bound Optimization BY Quadratic Approximation (BOBYQA) algorithm. According to the results, the maximum error percentage for the evolution of the susceptible, infected, and recovered populations in France was 0.05%, 19%, and 6%, respectively, while that for the evolution of the susceptible, infected, and recovered populations in Colombia was 0.003%, 19%, and 38%, respectively. This was considered for data in which the disease began to spread and human intervention did not imply a substantial change in the community.


Author(s):  
Fan Liu ◽  
Ya-Feng Liu ◽  
Ang Li ◽  
Christos Masouros ◽  
Yonina C. Eldar
Keyword(s):  

ETRI Journal ◽  
2020 ◽  
Vol 42 (5) ◽  
pp. 700-711
Author(s):  
Seungwoo Seo ◽  
Da‐Eun Ko ◽  
Jong‐Moon Chung

2020 ◽  
Vol 20 (4) ◽  
pp. 512-551
Author(s):  
JORI BOMANSON ◽  
TOMI JANHUNEN

AbstractAnswer set programming (ASP) is a paradigm for modeling knowledge-intensive domains and solving challenging reasoning problems. In ASP solving, a typical strategy is to preprocess problem instances by rewriting complex rules into simpler ones. Normalization is a rewriting process that removes extended rule types altogether in favor of normal rules. Recently, such techniques led to optimization rewriting in ASP, where the goal is to boost answer set optimization by refactoring the optimization criteria of interest. In this paper, we present a novel, general, and effective technique for optimization rewriting based on comparator networks which are specific kinds of circuits for reordering the elements of vectors. The idea is to connect an ASP encoding of a comparator network to the literals being optimized and to redistribute the weights of these literals over the structure of the network. The encoding captures information about the weight of an answer set in auxiliary atoms in a structured way that is proven to yield exponential improvements during branch-and-bound optimization on an infinite family of example programs. The used comparator network can be tuned freely, for example, to find the best size for a given benchmark class. Experiments show accelerated optimization performance on several benchmark problems.


Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1507
Author(s):  
Gaoming Du ◽  
Chao Tian ◽  
Zhenmin Li ◽  
Duoli Zhang ◽  
Chuan Zhang ◽  
...  

The delay bound in system on chips (SoC) represents the worst-case traverse time of on-chip communication. In network on chip (NoC)-based SoC, optimizing the delay bound is challenging due to two aspects: (1) the delay bound is hard to obtain by traditional methods such as simulation; (2) the delay bound changes with the different application mappings. In this paper, we propose a delay bound optimization method using discrete firefly optimization algorithms (DBFA). First, we present a formal analytical delay bound model based on network calculus for both unipath and multipath routing in NoCs. We then set every flow in the application as the target flow and calculate the delay bound using the proposed model. Finally, we adopt firefly algorithm (FA) as the optimization method for minimizing the delay bound. We used industry patterns (video object plane decoder (VOPD), multiwindow display (MWD), etc.) to verify the effectiveness of delay bound optimization method. Experiments show that the proposed method is both effective and reliable, with a maximum optimization of 42.86%.


AKSIOMA ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 13-23
Author(s):  
Sri Siti Supatimah ◽  
Farida Farida ◽  
Siska Andriani

Sentral Me Laundry is one of the service services businesses established in 2016 and has 2 employees having their address at Jalan pulau Ambon, Sukarame, Bandar Lampung. The development of laundry services in the middle of the city community indicates that laundry businesses can still develop and can achieve optimal profits. The purpose of this study was to find the optimal benefits obtained by the Sentral Me Laundry business. Errors in planning a laundry business result in a maximum profit. To prevent mistakes in planning a laundry business, it is necessary to use the right method. Banch And Bound Method (Integer Linear Programming) is a method that can be used to optimize laundry business by looking at the limited resources of the business. In the linear program method the decision variable can be a real number. While the optimization of laundry business that will be done requires a solution in the form of an integer called Integer. To help resolve cases of optimizing the benefits of laundry businesses using a computer program, QM For Windows. Calculations from the QM For Windows program produce 53 Kg of Badcover, 188 Kg of Doll, 1350 Kg of Clothing, 101 Kg of Blanket.. Keywords: Branch and Bound; Optimization; QM For Windows


Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4222
Author(s):  
Yumei Hu ◽  
Xuezhi Wang ◽  
Hua Lan ◽  
Zengfu Wang ◽  
Bill Moran ◽  
...  

We propose an iterative nonlinear estimator based on the technique of variational Bayesian optimization. The posterior distribution of the underlying system state is approximated by a solvable variational distribution approached iteratively using evidence lower bound optimization subject to a minimal weighted Kullback-Leibler divergence, where a penalty factor is considered to adjust the step size of the iteration. Based on linearization, the iterative nonlinear filter is derived in a closed-form. The performance of the proposed algorithm is compared with several nonlinear filters in the literature using simulated target tracking examples.


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