Finding the global optimal

2021 ◽  
pp. 43-48
Author(s):  
Owain Johnson
Keyword(s):  
Global Optimal

Automatic Registering and Stitching of TEM/STEM Image Mosaics

2013 ◽  
Author(s):  
Chung-Ching Lin ◽  
Franco Stellari ◽  
Lynne Gignac ◽  
Peilin Song ◽  
John Bruley
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Vlsi Circuit ◽  
Significant Progress ◽  
Research Areas ◽  
Automated Method ◽  
Transmission Electron ◽  
Stem Image ◽  
High Resolution Images ◽  
Global Optimal

Abstract Transmission Electron Microscopy (TEM) and scanning TEM (STEM) is widely used to acquire ultra high resolution images in different research areas. For some applications, a single TEM/STEM image does not provide enough information for analysis. One example in VLSI circuit failure analysis is the tracking of long interconnection. The capability of creating a large map of high resolution images may enable significant progress in some tasks. However, stitching TEM/STEM images in semiconductor applications is difficult and existing tools are unable to provide usable stitching results for analysis. In this paper, a novel fully automated method for stitching TEM/STEM image mosaics is proposed. The proposed method allows one to reach a global optimal configuration of each image tile so that both missing and false-positive correspondences can be tolerated. The experiment results presented in this paper show that the proposed method is robust and performs well in very challenging situations.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals A branch & bound algorithm to determine optimal bivariate splits for oblique decision tree induction

2021 ◽  
Author(s):  
Ferdinand Bollwein ◽  
Stephan Westphal
Keyword(s):  
Decision Tree ◽  
Feature Space ◽  
Classification Problems ◽  
Decision Tree Induction ◽  
Single Attribute ◽  
Global Optimal ◽  
The Individual ◽  
Tree Building ◽  
Very High ◽  
Multiclass Classification Problems

AbstractUnivariate decision tree induction methods for multiclass classification problems such as CART, C4.5 and ID3 continue to be very popular in the context of machine learning due to their major benefit of being easy to interpret. However, as these trees only consider a single attribute per node, they often get quite large which lowers their explanatory value. Oblique decision tree building algorithms, which divide the feature space by multidimensional hyperplanes, often produce much smaller trees but the individual splits are hard to interpret. Moreover, the effort of finding optimal oblique splits is very high such that heuristics have to be applied to determine local optimal solutions. In this work, we introduce an effective branch and bound procedure to determine global optimal bivariate oblique splits for concave impurity measures. Decision trees based on these bivariate oblique splits remain fairly interpretable due to the restriction to two attributes per split. The resulting trees are significantly smaller and more accurate than their univariate counterparts due to their ability of adapting better to the underlying data and capturing interactions of attribute pairs. Moreover, our evaluation shows that our algorithm even outperforms algorithms based on heuristically obtained multivariate oblique splits despite the fact that we are focusing on two attributes only.

scholarly journals Finite-Horizon Kinetic Energy Optimization of a Redundant Space Manipulator

2021 ◽  
Vol 11 (5) ◽  
pp. 2346
Author(s):  
Alessandro Tringali ◽  
Silvio Cocuzza
Keyword(s):  
Kinetic Energy ◽  
Real Time ◽  
Inverse Kinematics ◽  
Degrees Of Freedom ◽  
Computational Time ◽  
Redundant Manipulators ◽  
Space Robotics ◽  
Optimal Method ◽  
End Effector ◽  
Global Optimal

The minimization of energy consumption is of the utmost importance in space robotics. For redundant manipulators tracking a desired end-effector trajectory, most of the proposed solutions are based on locally optimal inverse kinematics methods. On the one hand, these methods are suitable for real-time implementation; nevertheless, on the other hand, they often provide solutions quite far from the globally optimal one and, moreover, are prone to singularities. In this paper, a novel inverse kinematics method for redundant manipulators is presented, which overcomes the above mentioned issues and is suitable for real-time implementation. The proposed method is based on the optimization of the kinetic energy integral on a limited subset of future end-effector path points, making the manipulator joints to move in the direction of minimum kinetic energy. The proposed method is tested by simulation of a three degrees of freedom (DOF) planar manipulator in a number of test cases, and its performance is compared to the classical pseudoinverse solution and to a global optimal method. The proposed method outperforms the pseudoinverse-based one and proves to be able to avoid singularities. Furthermore, it provides a solution very close to the global optimal one with a much lower computational time, which is compatible for real-time implementation.

scholarly journals An MEA-Tuning Method for Design of the PID Controller

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongli Zhang ◽  
Lijun Zhang ◽  
Zhiliang Dong
Keyword(s):  
Pid Controller ◽  
Industrial Process ◽  
Optimal Solution ◽  
Design Feature ◽  
Parameter Tuning ◽  
Parallel Structure ◽  
Evolutionary Information ◽  
Tuning Method ◽  
Efficiency And Effectiveness ◽  
Global Optimal

The optimization and tuning of parameters is very important for the performance of the PID controller. In this paper, a novel parameter tuning method based on the mind evolutionary algorithm (MEA) was presented. The MEA firstly transformed the problem solutions into the population individuals embodied by code and then divided the population into superior subpopulations and temporary subpopulations and used the similar taxis and dissimilation operations for searching the global optimal solution. In order to verify the control performance of the MEA, three classical functions and five typical industrial process control models were adopted for testing experiments. Experimental results indicated that the proposed approach was feasible and valid: the MEA with the superior design feature and parallel structure could memorize more evolutionary information, generate superior genes, and enhance the efficiency and effectiveness for searching global optimal parameters. In addition, the MEA-tuning method can be easily applied to real industrial practices and provides a novel and convenient solution for the optimization and tuning of the PID controller.

Holes Machining Process Optimization with Genetic Algorithm

2011 ◽  
Vol 460-461 ◽  
pp. 117-122 ◽  
Author(s):  
Guang Yu Zhu ◽  
Lian Fang Chen
Keyword(s):  
Genetic Algorithm ◽  
Cutting Tool ◽  
Machining Process ◽  
Processing Route ◽  
Optimal Route ◽  
Optimal Processing ◽  
Global Optimal ◽  
Level Method ◽  
Multi Level ◽  
Changing Times

In this paper, a multi-level method has been adopted to optimize the holes machining process with genetic algorithm (GA). Based on the analyzing of the features of the part with multi-holes, the local optimal processing route for the holes with the same processing feature is obtained with GA, then try to obtain the global optimal route with GA by considering the obtained local optimal route and the holes with different features. That is what the multi-level method means. The optimal route means the minimum moving length of the cutting tool and the minimum changing times of the cutting tool. The experiment is carried out to verify the algorithm and the proposed method, and result indicates that with GA and using the multi-level method the optimal holes machining route can be achieved efficiently.

The Nesting of Two-Dimensional Shapes Using Genetic Algorithms

1995 ◽  
Vol 209 (2) ◽  
pp. 115-124 ◽  
Author(s):  
H S Ismail ◽  
K K B Hon
Keyword(s):  
Genetic Algorithms ◽  
Evolutionary Process ◽  
Search Space ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Optimal Solutions ◽  
Spatial Constraints ◽  
Global Optimal ◽  
Optimum Solution ◽  
Rapid Conversion

The general two-dimensional cutting stock problem is concerned with the optimum layout and arrangement of two-dimensional shapes within the spatial constraints imposed by the cutting stock. The main objective is to maximize the utilization of the cutting stock material. This paper presents some of the results obtained from applying a combination of genetic algorithms and heuristic approaches to the nesting of dissimilar shapes. Genetic algorithms are stochastically based optimization approaches which mimic nature's evolutionary process in finding global optimal solutions in a large search space. The paper discusses the method by which the problem is defined and represented for analysis and introduces a number of new problem-specific genetic algorithm operators that aid in the rapid conversion to an optimum solution.

scholarly journals Best Proximity Point Results in Complex Valued Metric Spaces

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Binayak S. Choudhury ◽  
Nikhilesh Metiya ◽  
Pranati Maity
Keyword(s):  
Minimum Distance ◽  
Metric Spaces ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
Complex Valued Metric Space ◽  
Fixed Point Equation ◽  
Point Equation ◽  
Global Optimal ◽  
Metric Function ◽  
Complex Valued

We introduce the concept of proximity points for nonself-mappings between two subsets of a complex valued metric space which is a recently introduced extension of metric spaces obtained by allowing the metric function to assume values from the field of complex numbers. We apply this concept to obtain the minimum distance between two subsets of the complex valued metric spaces. We treat the problem as that of finding the global optimal solution of a fixed point equation although the exact solution does not in general exist. We also define and use the concept of P-property in such spaces. Our results are illustrated with examples.

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