scholarly journals New Methods for the Construction of Test Cases for Partitioning Heuristics

VLSI Design ◽  
1995 ◽  
Vol 3 (1) ◽  
pp. 93-98 ◽  
Author(s):  
Youssef Saab
Keyword(s):  
Integrated Circuits ◽  
Network Flow ◽  
Test Cases ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Clustering Problem ◽  
New Methods ◽  
Fixed Dimension ◽  
Geometric Clustering ◽  
Partitioning Problem

Partitioning is an important problem in the design automation of integrated circuits. This problem in many of its formulation is NP-Hard, and several heuristic methods have been proposed for its solution. To evaluate the effectiveness of the various partitioning heuristics, it is desirable to have test cases with known optimal solutions that are as “random looking” as possible. In this paper, we describe several methods for the construction of such test cases. All our methods except one use the theory of network flow. The remaining method uses a relationship between a partitioning problem and the geometric clustering problem. The latter problem can be solved in polynomial time in any fixed dimension.

scholarly journals Software Test Data Generation based on Path Testing using Genetic Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4466-4473
Keyword(s):  
Genetic Algorithms ◽  
Test Data ◽  
Iterative Algorithms ◽  
Continuous Functions ◽  
Software Test ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Optimal Solutions ◽  
Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

Application of the optimal network flow method to the problem of locomotive selection for freight trains on the Eastern Polygon

2019 ◽  
pp. 94-106
Author(s):  
Александр Борисович Шабунин ◽  
Андрей Куркенович Такмазьян
Keyword(s):  
Network Flow ◽  
Optimal Solution ◽  
Global Optimum ◽  
Full Scale ◽  
Scale Model ◽  
Test Cases ◽  
Processor Time ◽  
Flow Method ◽  
Optimal Network ◽  
Locomotive Assignment

Моделируется подбор тяговых ресурсов (локомотивов - в данном случае) для провоза грузовых поездов. В качестве входных данных рассматриваются маршрут поезда, время готовности поезда к отправлению, средняя скорость и вес поезда. Имеется множество локомотивов, обладающих грузоподъемностью и областью разрешенного действия. Цель - оптимально подобрать ресурс для каждого участка маршрута поезда. Решение ищется методом потока ресурсов минимальной суммарной стоимости через специально сконструированную сеть. Сеть построена на основе взвешенного орграфа из ребер графика поездов на линейных участках и ребер альтернативы, в процессе прохода по которым осуществляется “смена деятельности” локомотива (например, отцепление от одного поезда и подцепка к другому). Полученное решение обладает свойством глобальной оптимальности по времени. The selection of traction resources (locomotives) for the transport of freight trains is modelled. The input data are the train route, the readiness time of the train for departure, the average speed and weight of the train. In addition, there are many locomotives with a carrying capacity and an area of permitted action. The research objective is to optimally select a resource for each segment of the train route. The solution is sought by the resource flow method of the minimum total cost through a specially designed network. The network includes edges created from train schedule segments whose filling means locomotive assignment to train at the segment, and special alternative edges, passing through which a locomotive alternates its assignment. The algorithm for finding the optimal solution is the method of pushing through the pre-flow proposed by A. Goldberg and R. Tarjan. This is one of the fastest algorithms converging to a global optimum. Two test cases were investigated: a trivial one, out of six trains and three locomotives, and a more complicated one, which is a model example the size of 10% of the full scale model and consists of 150 trains. Full scale calculations provide planning of the freight transportation on the Eastern Operational domain of the Russian Railways. The model includes 1800 locomotives and about 3000 trains on the time horizon of 48 hours. Solution is found in less than 5 minutes of processor time for a PC powered by Intel(R) Pentium(R) G2010 2.80 GHz processor.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

Optimizing Energy Output of Piezoelectric Microcantilevers

2011 ◽  
Author(s):  
Mehdi Rezaeisaray ◽  
Don Raboud ◽  
Walied Moussa
Keyword(s):  
Output Voltage ◽  
Proof Mass ◽  
Geometry Optimization ◽  
Electrical Energy ◽  
Electrical Circuit ◽  
Energy Output ◽  
Test Cases ◽  
New Methods ◽  
Piezoelectric Cantilever ◽  
Piezoelectric Cantilevers

This work presents some new methods in optimizing electrical energy, harvested using a micro piezoelectric cantilever. Both mechanical and electrical aspects have been considered. Mechanically, two items have been considered to maximize the generated voltage: geometry of the cantilever and placement of the electrodes. It has been shown that for given sizes of length and width of the harvester and for a given natural frequency, the output voltage can be increased by adjusting the thickness of the beam and the proof mass and consequently increasing the amplitude of vibration. As well, the placement of the electrodes plays a very important role in optimizing output voltage. It has also been shown that piezoelectric cantilevers with shorter top electrodes induce higher voltage than cantilevers with longer top electrodes. Overall results agree with the analytical equations reported in literature so far. Moreover, distribution of top electrodes along the width of the cantilever has been taken into consideration. It has been shown how output voltage can be approximately doubled by using two narrower top electrodes along the width of the cantilever. All analysis in this work was carried out in ANSYS. In this research, to improve the electrical efficiency, diodes have been considered in the circuit to reduce electrical losses in comparison to rectifiers which have been used in conventional harvesters. Applying these methods to particular test cases, a 71% increase in output voltage was observed for the case of geometry optimization, a 116% increase was observed for the case of shortening the top electrode and losses in the electrical circuit were reduced by approximately 50% by using diodes comparing to using rectifiers. While these results focused on cantilever based harvesters, the ideas contained are equally applicable to other structures.

A framework to aggregate multiple ontology matchers

2019 ◽  
Vol 16 (2) ◽  
pp. 151-169 ◽  
Author(s):  
Jairo Francisco de Souza ◽  
Sean Wolfgand Matsui Siqueira ◽  
Bernardo Nunes
Keyword(s):  
Equation System ◽  
Test Cases ◽  
Semantic Heterogeneity ◽  
Ontology Alignment ◽  
Data Set ◽  
Similarity Functions ◽  
Content Type ◽  
New Methods ◽  
Linear Equation System ◽  
Ontology Alignment Evaluation Initiative

Purpose Although ontology matchers are annually proposed to address different aspects of the semantic heterogeneity problem, finding the most suitable alignment approach is still an issue. This study aims to propose a computational solution for ontology meta-matching (OMM) and a framework designed for developers to make use of alignment techniques in their applications. Design/methodology/approach The framework includes some similarity functions that can be chosen by developers and then, automatically, set weights for each function to obtain better alignments. To evaluate the framework, several simulations were performed with a data set from the Ontology Alignment Evaluation Initiative. Simple similarity functions were used, rather than aligners known in the literature, to demonstrate that the results would be more influenced by the proposed meta-alignment approach than the functions used. Findings The results showed that the framework is able to adapt to different test cases. The approach achieved better results when compared with existing ontology meta-matchers. Originality/value Although approaches for OMM have been proposed, it is not easy to use them during software development. On the other hand, this work presents a framework that can be used by developers to align ontologies. New ontology matchers can be added and the framework is extensible to new methods. Moreover, this work presents a novel OMM approach modeled as a linear equation system which can be easily computed.

scholarly journals Improved upper bounds in clique partitioning problem

2019 ◽  
pp. 93-104
Author(s):  
Alexander B. Belyi ◽  
Stanislav L. Sobolevsky ◽  
Alexander N. Kurbatski ◽  
Carlo Ratti
Keyword(s):  
Exact Solution ◽  
Branch And Bound ◽  
Optimization Problems ◽  
Weighted Graph ◽  
Upper Bounds ◽  
Clique Partitioning ◽  
Clustering Problem ◽  
Combinatorial Optimization Problems ◽  
Clique Partitioning Problem ◽  
Partitioning Problem

In this work, a problem of partitioning a complete weighted graph into cliques in such a way that sum of edge weights between vertices belonging to the same clique is maximal is considered. This problem is known as a clique partitioning problem. It arises in many applications and is a varian of classical clustering problem. However, since the problem, as well as many other combinatorial optimization problems, is NP-hard, finding its exact solution often appears hard. In this work, a new method for constructing upper bounds of partition quality function values is proposed, and it is shown how to use these upper bounds in branch and bound technique for finding an exact solution. Proposed method is based on the usage of triangles constraining maximal possible quality of partition. Novelty of the method lies in possibility of using triangles overlapping by edges, which allows to find much tighter bounds than when using only non-overlapping subgraphs. Apart from constructing initial estimate, a method of its recalculation, when fixing edges on each step of branch and bound method, is described. Test results of proposed algorithm on generated sets of random graphs are provided. It is shown, that version that uses new bounds works several times faster than previously known methods.

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