scholarly journals Cuckoo Search with Lévy Flights for Weighted Bayesian Energy Functional Optimization in Global-Support Curve Data Fitting

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Akemi Gálvez ◽  
Andrés Iglesias ◽  
Luis Cabellos
Keyword(s):  
Optimization Problems ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Well Being ◽  
Data Fitting ◽  
Energy Functional ◽  
Simple Task ◽  
Cad Cam ◽  
Two Parameters ◽  
Applied Fields

The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm calledcuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.

scholarly journals Firefly Algorithm for Polynomial Bézier Surface Parameterization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Akemi Gálvez ◽  
Andrés Iglesias
Keyword(s):  
Computer Animation ◽  
Computer Aided Design ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Well Being ◽  
Optimization Techniques ◽  
Surface Parameterization ◽  
Cad Cam ◽  
Data Points

A classical issue in many applied fields is to obtain an approximating surface to a given set of data points. This problem arises in Computer-Aided Design and Manufacturing (CAD/CAM), virtual reality, medical imaging, computer graphics, computer animation, and many others. Very often, the preferred approximating surface is polynomial, usually described in parametric form. This leads to the problem of determining suitable parametric values for the data points, the so-called surface parameterization. In real-world settings, data points are generally irregularly sampled and subjected to measurement noise, leading to a very difficult nonlinear continuous optimization problem, unsolvable with standard optimization techniques. This paper solves the parameterization problem for polynomial Bézier surfaces by applying the firefly algorithm, a powerful nature-inspired metaheuristic algorithm introduced recently to address difficult optimization problems. The method has been successfully applied to some illustrative examples of open and closed surfaces, including shapes with singularities. Our results show that the method performs very well, being able to yield the best approximating surface with a high degree of accuracy.

scholarly journals A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1840
Author(s):  
Nicolás Caselli ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Sergio Valdivia ◽  
Rodrigo Olivares
Keyword(s):  
Machine Learning ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Parameter Configuration ◽  
Self Tuning ◽  
Problem Solvers

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

Exploratory cuckoo search for solving single-objective optimization problems

2021 ◽  
Author(s):  
Bilal H. Abed-alguni ◽  
Noor Aldeen Alawad ◽  
Malek Barhoush ◽  
Rafat Hammad
Keyword(s):  
Optimization Problems ◽  
Cuckoo Search ◽  
Single Objective

PSO and GA tuned conventional and fractional order PID controllers for quadrotor control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hasan Saribas ◽  
Sinem Kahvecioglu
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Optimization Problems ◽  
Parameter Tuning ◽  
Absolute Error ◽  
Cost Functions ◽  
Pid Controllers ◽  
Content Type ◽  
Quadrotor Control

Purpose This study aims to compare the performance of the conventional and fractional order proportional-integral-derivative (PID and FOPID) controllers tuned with a particle swarm optimization (PSO) and genetic algorithm (GA) for quadrotor control. Design/methodology/approach In this study, the gains of the controllers were tuned using PSO and GA, which are included in the heuristic optimization methods. The tuning processes of the controller’s gains were formulated as optimization problems. While generating the objective functions (cost functions), four different decision criteria were considered separately: integrated summation error (ISE), integrated absolute error, integrated time absolute error and integrated time summation error (ITSE). Findings According to the simulation results and comparison tables that were created, FOPID controllers tuned with PSO performed better performances than PID controllers. In addition, the ITSE criterion returned better results in control of all axes except for altitude control when compared to the other cost functions. In the control of altitude with the PID controller, the ISE criterion showed better performance. Originality/value While a conventional PID controller has three parameters (Kp, Ki, Kd) that need to be tuned, FOPID controllers have two additional parameters (µ). The inclusion of these two extra parameters means more flexibility in the controller design but much more complexity for parameter tuning. This study reveals the potential and effectiveness of PSO and GA in tuning the controller despite the increased number of parameters and complexity.

scholarly journals Experimental Design for Optimization of Orthogonal Projection Pursuit Models

2020 ◽  
Vol 34 (06) ◽  
pp. 10235-10242
Author(s):  
Mojmir Mutny ◽  
Johannes Kirschner ◽  
Andreas Krause
Keyword(s):  
Experimental Design ◽  
Orthogonal Projection ◽  
Optimization Problems ◽  
Additive Model ◽  
Projection Pursuit ◽  
Parameter Tuning ◽  
Additive Models ◽  
Function Optimization ◽  
Bayesian Optimization ◽  
Additive Structure

Bayesian optimization and kernelized bandit algorithms are widely used techniques for sequential black box function optimization with applications in parameter tuning, control, robotics among many others. To be effective in high dimensional settings, previous approaches make additional assumptions, for example on low-dimensional subspaces or an additive structure. In this work, we go beyond the additivity assumption and use an orthogonal projection pursuit regression model, which strictly generalizes additive models. We present a two-stage algorithm motivated by experimental design to first decorrelate the additive components. Subsequently, the bandit optimization benefits from the statistically efficient additive model. Our method provably decorrelates the fully additive model and achieves optimal sublinear simple regret in terms of the number of function evaluations. To prove the rotation recovery, we derive novel concentration inequalities for linear regression on subspaces. In addition, we specifically address the issue of acquisition function optimization and present two domain dependent efficient algorithms. We validate the algorithm numerically on synthetic as well as real-world optimization problems.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

scholarly journals THE CORRELATION OF ARTERIAL CORD BLOOD GAS VALUES AND APGAR SCORES IN TERM BABIES WITHOUT FETAL DISTRESS AND THEIR EFFECTS ON NICU ADMISSIONS

2021 ◽  
Author(s):  
ASLAN YILMAZ ◽  
Nesrin Kaya ◽  
Ipek Ulkersoy ◽  
Zeynep Alp Ünkar ◽  
Hazal Cansu ACAR ◽  
...  
Keyword(s):  
Cord Blood ◽  
Neonatal Intensive Care ◽  
Umbilical Artery ◽  
Fetal Distress ◽  
Well Being ◽  
Blood Gas ◽  
Threshold Values ◽  
Apgar Scores ◽  
Nicu Admission ◽  
Two Parameters

Abstract Background Umbilical artery cord blood gas (UACG) values and Apgar scores (AS) are the two parameters that provide the fastest information about the well-being of a baby after birth. We hypothesized that AS may not be sufficient for a complete and correct evaluation of the newborns and UACG should be used routinely for all births even without any signs of fetal distress. Material-methods In this retrospective study, the data of 1781 babies born between January 2018 and December 2019 at Cerrahpasa Faculty of Medicine were analyzed. Newborn with fetal distress, congenital anomalies, severe and moderate acidemia (pH ≤7.1 at UACG), and pre and postterm newborns are excluded. The UACG and the 1 and 5-minute AS data of 1438 cases were evaluated. Mild acidemia was accepted as a pH between 7.1 and 7.2. Following UACG threshold values were accepted as abnormal pH <7.2, BE <-6 mmol/l, lactate ≥5 mmol/l, HCO3 <18 mmol/l, pCO2 ≥50 mmHg. We evaluated the correlation between UACG and 1 and 5-minute AS and their effects on admission to neonatal intensive care unit (NICU). Results There was a significant correlation between both 1 and 5-minute AS and UACG values such as pH, lactate, and pCO 2 (p<0.001). In addition, significant correlation was found between the 5-minute AS below 7 and some UACG abnormal threshold values (pH, HCO 3 , base excess) (p<0.001). We found that some cases with mild acidemia had a normal 1 and 5-minute Apgar scores (AS≥7) in %1.9 and %2 of cases, respectively. A significant correlation was found between admissions to NICU with 1 and 5-minute AS of <7 (p<0.001). Conclusions The 5-minute AS of 7 or higher may not be sufficient to verify the well-being of a newborn. Relying only on AS, may create the risk of missing some newborns with mild metabolic acidosis. 1 and 5-minute AS could be used as a predictive value for NICU admission. We propose that routine UACG should be evaluated for each birth at term, even without any signs of fetal distress and normal AS.

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