Mr. Anurag Sharma

Title: Assistant Lecturer

Room #: C139

DIVISION: Computing Science/Information Systems

Email: sharma_au(at)usp.ac.fj

Phone: 323 2329

Fax: 323 1527

Visit Personal Homepages for more Details

Courses Teaching this Semester

1. CS240
2. CS112

Teaching Timetable

1. CS240: Mon 1-2pm, Wed 12-1pm, Wed 12-1pm, Thu 1-2pm
2. CS112: Mon 12-1pm, Tue 10-11am, Wed 1-2pm

Office Hours

1. CS112:    Tue   11-12pm
2. CS240:    Wed  11-12pm

USP/SCIMS Contributions

Fire Warden rep

Most recent research activities

Development of a novel algorithm for optimization purpose

Research Interests

My research interest is mainly in Artificial Intelligence and new optimization techniques in engineering specifically with the use of heuristic algorithms. Currently, I am working on inductive modeling through Group Method of Data Handling (GMDH). I worked in the following research projects:

.        Clustering for Data Mining: A Hybrid Particle Swarm Optimization -Self Organizing Map Approach
A novel algorithm was proposed that uses Particle Swarm Optimization (PSO) algorithm to determine the cluster boundaries automatically in the output of self-organizing map (SOM). SOM is a data mining tool that reveals structure in data sets through data visualization and PSO is one of adaptive heuristic algorithms to solve many kinds of continuous and binary problems of large domain whose problem formulation is either impossible or very time consuming to execute.

.        Use of Particle Swarm Optimization in Engineering Problems
PSO was used to solve several problems like flow-shop scheduling problem of factories, Dynamic pick and place problem in robotics and vehicle routing problem.

.        Inductive Modeling through GMDH and PSO
We proposed a new design methodology which is based on hybrid of particle swarm optimization (PSO) and group method of data handling (GMDH). The PSO and GMDH are two well-known nonlinear methods of mathematical modeling. This novel method constructs a GMDH network model of a population of promising PSO solutions. The new PSO-GMDH hybrid implementation is then can be applied to modeling and prediction of practical datasets.

Selected Publications

1. A. Sharma and C. W. Omlin, Performance comparison of Particle Swarm Optimization with traditional clustering algorithms used in Self Organizing Map, International Journal of Computational Intelligence, vol. 5, No. 1.1, pp. 1-12, 2008
2. G. C. Onwubolu, A. Sharma, A. Dayal, D. Bhartu, A. Shankar, and K. Katafono, Hybrid Particle Swarm Optimization and Group Method of Data Handling for Inductive Modeling, International Conference on Inductive Modeling, Kyiv, Ukraine, September 15-19, 2008
3. A. Sharma and G. Onwubolu, Hybrid Particle Swarm Optimization and GMDH System, In: (ed. Onwubolu, G. C.) Hybrid Self-Organizing Modeling Systems, Springer-Verlag , Germany (accepted, October 2008)
4. A. Sharma and C. W. Omlin, "Determining cluster boundary using Particle Swarm Optimization," International Conference in Neural Networks, published as Proceedings World Enformatika Society, Transactions on Engineering, Computing and Technology, vol. 15, pp. 250-254, 2006
5. G. C. Onwubolu and A. Sharma, Particle Swarm Optimization for the assignment of facilities to locations. New Optimization Techniques in Engineering, Springer-Verlag, 2004