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Trafali̇s, Theodore B.; Kasap, Suat

doi:10.1023/a:1015564423757

Cited by 27 publications

(2 citation statements)

References 29 publications

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“…With this property, the searching process can cover a wider search space within a small number of iterations. This is a different property compared to improvement heuristic searches such as the genetic algorithm, the tabu search, etc [21]. Generally, an improvement heuristic searches algorithm needs a larger number of iterations to increase the 'visited feasible solution' around the search space, in which the computational cost becomes problematic.…”

Section: Performance Improvementmentioning

confidence: 99%

Performance improvement for optimization of the non-linear geometric fitting problem in manufacturing metrology

Moroni

Syam

Petrò

2014

Meas. Sci. Technol.

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Product quality is a main concern in today manufacturing. It is a fundamental requirement for companies to be competitive. To assure such quality, a dimensional inspection to verify geometric property of a product has to be carried out. High speed non contact scanners help this task, by both speeding up acquisition speed and increasing accuracy through a more complete description of the surface. The algorithms for the management of the measurement data play a critical role in ensuring both the measurement accuracy and speed. One of the most fundamental parts of the algorithm is procedure for fitting substitute geometry to the cloud of points. This article addresses this challenge. Three relevant geometries are selected as case studies: non-linear least-square fitting of circle, sphere and cylinder. These geometries are chosen with consideration of their common use in practice; for example the sphere is often adopted as reference artifact for performance verification of coordinate measuring machine (CMM) and cylinder is the most relevant geometry for pin-hole relation as an assembly feature to construct a complete functioning product.In this article, an improvement of the initial point guess for Levenberg-Marquardt (LM) algorithm by employing Chaos Optimization (CO) method is proposed. This causes a performance improvement in the optimization of a non-linear function fitting the three geometries. The results show that, with this combination, higher quality of fitting results in term of smaller norm of the residuals can be obtained while preserving the computational cost. Fitting a "incomplete-point-cloud", which is a situation where the point cloud do not cover a complete feature e.g. from half of the total part surface, is also investigated. Finally, a case study about fitting a hemisphere is presented.

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Section: Performance Improvementmentioning

confidence: 99%

Performance improvement for optimization of the non-linear geometric fitting problem in manufacturing metrology

Moroni

Syam

Petrò

2014

Meas. Sci. Technol.

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“…Ant colony optimization, bee colony optimization, intelligent water drops algorithms(IWD), cuckoo search algorithm are some examples of intelligent approaches. A lot of work has been done in the area of metaheuristics (Trafali & Suat, 2002) in past few years that also affect MJQO solution approaches. Some of best solutions have been proposed, which are based on metaheuristics approaches viz.…”

Section: Related Workmentioning

confidence: 99%

Query Optimization

Joshi

Srivastava

2013

International Journal of Intelligent Information Technologies

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Query optimization is an important aspect in designing database management systems, aimed to find an optimal query execution plan so that overall time of query execution is minimized. Multi join query ordering (MJQO) is an integral part of query optimizer. This paper aims to propose a solution for MJQO problem, which is an NP complete problem. This paper proposes a heuristic based algorithm as a solution of MJQO problem. The proposed algorithm is a combination of two basic search algorithms, cuckoo and tabu search. Simulation shows some exciting results in favour of the proposed algorithm and concludes that proposed algorithm can solve MJQO problem in less amount of time than the existing methods.

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A novel global optimization technique for high dimensional functions

Groşan

Abraham

2009

Int. J. Intell. Syst.

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Several types of line search methods are documented in the literature and are well known for unconstraint optimization problems. This paper proposes a modified line search method, which makes use of partial derivatives and restarts the search process after a given number of iterations by modifying the boundaries based on the best solution obtained at the previous iteration (or set of iterations). Using several high-dimensional benchmark functions, we illustrate that the proposed line search restart (LSRS) approach is very suitable for high-dimensional global optimization problems. Performance of the proposed algorithm is compared with two popular global optimization approaches, namely, genetic algorithm and particle swarm optimization method. Empirical results for up to 2000 dimensions clearly illustrate that the proposed approach performs very well for the tested high-dimensional functions. C 2009 Wiley Periodicals, Inc.

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Cited by 27 publications

References 29 publications

Performance improvement for optimization of the non-linear geometric fitting problem in manufacturing metrology

Performance improvement for optimization of the non-linear geometric fitting problem in manufacturing metrology

Query Optimization

A novel global optimization technique for high dimensional functions

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