Multi-Objective Optimization of Surface Grinding Process Using NSGA II

Kodali, Shyam Prasad; Kudikala, Rajesh; Deb, Kalyanmoy

doi:10.1109/icetet.2008.139

Cited by 21 publications

(13 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers had already solved the multiobjective optimization problems in machining processes using well-known posteriori approaches such as NSGA, NSGA-II, MOGA, and MODE (Mitra and Gopinath 2004;Kuriakose and Shunmugam 2005;Konak et al 2006;Mandal et al 2007;Kodali et al 2008;Kanagarajan et al 2008;Palanikumar et al 2009;Datta and Deb 2009;Yang and Natarajan 2010;Senthilkumar et al 2010Senthilkumar et al , 2011Joshi and Pande 2011;Mitra 2009;Acharya et al 2013). However, these algorithms require tuning of algorithm-specific parameters and improper tuning of algorithm-specific parameters may lead to non-Pareto optimal solutions.…”

Section: Multiobjective Optimization Of Machining Processesmentioning

confidence: 99%

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

View full text Add to dashboard Cite

Multiobjective optimization aspects of a traditional machining process namely surface grinding and five modern machining processes namely, wire-electro discharge machining process, micro-wire-electric discharge machining process, laser cutting process, electrochemical machining process, and electrochemical discharge machining process are presented in this chapter. A posteriori multiobjective optimization algorithm named as nondominated sorting teaching-learning-based optimization (NSTLBO) algorithm is proposed to solve the multiobjective optimization problems of the machining processes. A Pareto optimal set of solutions along with a Pareto front is presented for each of the considered machining processes.

show abstract

Section: Multiobjective Optimization Of Machining Processesmentioning

confidence: 99%

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

View full text Add to dashboard Cite

show abstract

“…Kodali et al [24] and Jianling [20] used optimization technique of NSGA-II to simulate a number of test problems from previous studies. They claimed that the performance of this technique is better than Pareto-archived evolution strategy (PAES) and strength Pareto EA (SPEA) in terms of converging near the true Pareto-optimal set and finding a diverse set of solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms

Ahmadi

Zandieh

Farrokh

et al. 2016

Computers & Operations Research

194

View full text Add to dashboard Cite

“…The thresholds for symmetry detection (see Table 4) are fixed for all the results shown in this paper. The NSGA-II [31] searches the parameter space to find an optimal segmentation, measured by both the symmetry evaluation (17) and the supervised or unsupervised segmentation evaluation ((13) or (11)). All the segmentations shown in experiments are optimized.…”

Section: The Symmetry Evaluationmentioning

confidence: 99%

“…It is formulated as a multi-objective optimization (MOP), which is the process of optimizing multiple objectives subject to certain constraints. We use Nondominated Sorting Genetic Algorithm (NSGA-II) [31], a multi-objective optimization algorithm to search for optimum matched segmentation parameters ( g and m ) by using measures of the objective functions of segmentation and symmetry (see Section 3.3). Our optimization problem (see Fig.…”

Section: Multi-objective Optimization For Segmentation and Symmetrymentioning

confidence: 99%

Reflection Symmetry-Integrated Image Segmentation

Sun

Bhanu

2012

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.

show abstract

Multi-Objective Optimization of Surface Grinding Process Using NSGA II

Cited by 21 publications

References 8 publications

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

Multiobjective Optimization of Machining Processes Using NSTLBO Algorithm

A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms

Reflection Symmetry-Integrated Image Segmentation

Contact Info

Product

Resources

About