Qualification: 
Ph.D, MCA
Email: 
s_thangavel@cb.amrita.edu

Dr. Thangavelu S. currently serves as Assistant Professor at Department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. His areas of research include Evolutionary Algorithms and Database Technologies.

Publications

Publication Type: Journal Article

Year of Publication Publication Type Title

2017

Journal Article

A. Sn, Dr. Shriram K Vasudevan, Prashant R. Nair, Dr. Thangavelu S., and Rmd, S., “A proposal for mitigating fishermen killing in indian sea borders through technology – maritime boundary identification device”, Indonesian Journal of Electrical Engineering and Computer Science, vol. 6, pp. 704-710, 2017.[Abstract]


The Tamil Nadu – Sri Lanka maritime boundary has been responsible for frequent controversies in the global front, due to fishermen from Rameshwaram and Ramanthapuram districts recklessly straying past Indian waters. Instances of Indian fishermen being captured and killed by the Sri Lankan navy have spun vivid images of violence and human rights violation among the masses. Social activists are desperate for an automatic alarm system to warn the fishermen when they are about to cross the border, and avert a possible impending crisis. The following discussion focuses on the design of an alarm signal system that could alert the fishermen on a periodic basis as they approach closer to the maritime boundary. Installing Global Positioning System (GPS) devices would pose several economic challenges. Instead of the conventional approach of using GPS devices to track location, a transmitter – receiver system exquisitely designed to send signals to the boat would be effective. This system helps in keeping a continuous track of the boats. It provides a reliable solution to alert fishermen before they could inadvertently exceed the boundary. © 2017 Institute of Advanced Engineering and Science. All rights reserved.

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2015

Journal Article

Dr. Thangavelu S., Dr. Jeyakumar G., and Dr. Shunmuga Velayutham C., “Population variance based empirical analysis of the behavior of differential evolution variants”, Applied Mathematical Sciences, vol. 9, pp. 3249-3263, 2015.[Abstract]


Differential Evolution (DE) is a simple but efficient Evolutionary Algorithm (EA) for stochastic real parameter optimization. With various types of mutation and crossover applicable to DE, there exist many variants of DE. The empirical comparisons between the performances of these variants on chosen benchmarking problems are well reported in literature. However, attempts to analyze the reason for such identified behavior of the variants are scarce. As an attempt in this direction, this paper empirically analyzes the performance as well as the reason for such performance of 14 classical DE variants on 4 benchmarking functions with different modality and decomposability. The empirical analysis is carried out by measuring the mean objective function values (MOV), success rate (Sr), probability of convergence (Pc), quality measure (Qm) and empirical evolution of the variance of the population (Evar). The study also includes reporting evidences for the variants suffering with stagnation and/or premature convergence. © 2014 S. Thangavelu, G. Jeyakumar and C. Shunmuga Velyautham.

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2015

Journal Article

Dr. Thangavelu S. and Dr. Shunmuga Velayutham C., “An investigation on mixing heterogeneous differential evolution variants in a distributed framework”, International Journal of Bio-Inspired Computation, vol. 7, pp. 307-320, 2015.[Abstract]


This paper attempts a preliminary investigation to gain insight about the cooperative dynamics of mixing the four classical differential evolution (DE) variants viz. DE/rand/1/bin, DE/best/1/bin, DE/rand/2/bin and DE/best/2/bin in an island-based distributed framework. The exhaustive combinations of the above said four DE variants in an island size of 4, resulting in 35 distributed DE variants, have all been implemented and tested on 14 unconstrained test functions with diverse features grouped by their modality and decomposability. Simulation results show that the rand-best variants' mixing, display a better cooperative characteristics than rand-rand and best-best variants' mixing. This insight motivated for further investigations on mixing DE/rand-to-best/1/bin (a variant which intrinsically employs rand and best strategies) with DE/rand/1/bin and DE/best/1/bin in the distributed framework. Simulation results reiterated the observations about the cooperative characteristics of rand-best variants' combinations with the latter mixing showing still better cooperative characteristics both in terms of probability of convergence and convergence rate. Copyright © 2015 Inderscience Enterprises Ltd.

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2010

Journal Article

Dr. Thangavelu S. and Dr. Shunmuga Velayutham C., “Taguchi method based parametric study of generalized generation gap genetic algorithm model”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6466 LNCS, pp. 344-350, 2010.[Abstract]


In this paper, a parametric study of Generalized Generation Gap (G3) Genetic Algorithm (GA) model with Simplex crossover (SPX) using Taguchi method has been presented. Population size, number of parents and offspring pool size are considered as design factors with five levels. The analysis of mean factor is conducted to find the influence of design factors and their optimal combination for six benchmark functions. The experimental results suggest more experiments on granularity of design factor levels for better performance efficacy. © 2010 Springer-Verlag. More »»
Faculty Research Interest: 
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