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Waghmare G.,Kk Wagh Institute Of Engineering Education And Research
Information Sciences | Year: 2013

A note published by Črepinšek et al. [3] (A note on teaching-learning-based optimization algorithm, Information Sciences 212 (2012) 79-93) reported three "important mistakes" regarding teaching-learning-based optimization (TLBO) algorithm. Furthermore, the authors had presented some experimental results for constrained and unconstrained benchmark functions and tried to invalidate the performance supremacy of the TLBO algorithm. However, the authors had not reviewed the latest research literature on TLBO algorithm and their observations about TLBO algorithm were based only on two papers that were published initially. The views and the experimental results presented by Črepinšek et al. [3] are questionable and this paper re-examines the experimental results and corrects the understanding about the TLBO algorithm in an objective manner. The latest literature on TLBO algorithm is also presented and the algorithm-specific parameter-less concept of TLBO is explained. The results of the present work demonstrate that the TLBO algorithm performs well on the problems where the fitness-distance correlations are low by proper tuning of the common control parameters of the algorithm. © 2012 Elsevier Inc. All rights reserved. Source

Munje R.K.,Kk Wagh Institute Of Engineering Education And Research | Parkhe J.G.,Shri Guru Gobind Singhji Institute of Engineering and Technology | Patre B.M.,Shri Guru Gobind Singhji Institute of Engineering and Technology
Annals of Nuclear Energy | Year: 2015

Xenon induced spatial oscillations developed in large nuclear reactors, like Advanced Heavy Water Reactor (AHWR) need to be controlled for safe operation. Otherwise, a serious situation may arise in which different regions of the core may undergo variations in neutron flux in opposite phase. If these oscillations are left uncontrolled, the power density and rate of change of power at some locations in the reactor core may exceed their respective thermal limits, resulting in fuel failure. In this paper, a state feedback based control strategy is investigated for spatial control of AHWR. The nonlinear model of AHWR including xenon and iodine dynamics is characterized by 90 states, 5 inputs and 18 outputs. The linear model of AHWR, obtained by linearizing the nonlinear equations is found to be highly ill-conditioned. This higher order model of AHWR is first decomposed into two comparatively lower order subsystems, namely, 73rd order 'slow' subsystem and 17th order 'fast' subsystem using two-stage decomposition. Composite control law is then derived from individual subsystem feedback controls and applied to the vectorized nonlinear model of AHWR. Through the dynamic simulations it is observed that the controller is able to suppress xenon induced spatial oscillations developed in AHWR and the overall performance is found to be satisfactory. © 2014 Elsevier Ltd. Source

Gangurde S.R.,Kk Wagh Institute Of Engineering Education And Research | Akarte M.M.,Indian National Institute of Engineering
IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management | Year: 2010

In this research paper the alternatives of vacuum cleaners are ranked using MADM methods such as Simple Additive Weighing (SAW) Method, Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, Modified TOPSIS, Grey Relational Analysis (GRA) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The results of various methods are then compared and a new quantitative approach has been suggested in case of tie. It is observed that the proposed quantitative approach provides better guidelines to the decision maker, than that provided by qualitative approach applied by earlier researchers. ©2010 IEEE. Source

Venkata Rao R.,Sardar Vallabhbhai National Institute of Technology, Surat | Pawar P.J.,Kk Wagh Institute Of Engineering Education And Research
Applied Soft Computing Journal | Year: 2010

The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques. © 2009 Elsevier B.V. All rights reserved. Source

Pawar P.J.,Kk Wagh Institute Of Engineering Education And Research | Rao R.V.,Sardar Vallabhbhai National Institute of Technology, Surat
International Journal of Advanced Manufacturing Technology | Year: 2013

The optimum selection of process parameters plays a significant role to ensure quality of product, to reduce the machining cost and to increase the productivity of any machining process. This paper presents the optimization aspects of process parameters of three machining processes including an advanced machining process known as abrasive water jet machining process and two important conventional machining processes namely grinding and milling. A recently developed advanced optimization algorithm, teaching-learning-based optimization (TLBO), is presented to find the optimal combination of process parameters of the considered machining processes. The results obtained by using TLBO algorithm are compared with those obtained by using other advanced optimization techniques such as genetic algorithm, simulated annealing, particle swarm optimization, harmony search, and artificial bee colony algorithm. The results show better performance of the TLBO algorithm. © 2012 Springer-Verlag London. Source

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