TGI Fernando

University of Sri Jayewardenepura, Computer Science, Faculty Member

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Educational Qualifications:
B.Sc. (Mathematics)(USJ)(Sri Lanka) – 1993
M.Sc. (Industrial Mathematics)(USJ) (Sri Lanka) – 1998
M.Sc. (Computer Science)(AIT)(Thailand) – 2002
Ph.D. (Intelligent Systems)(Brunel)(UK) – 2009

Supervision work:
MPhil (Computer Science), Dilini Thalagala, Human Age Classification using Deep Architectures (ongoing research project)
MPhil (Computer Science), MKA Ariyaratne, Designing and Developing Nature-Inspired Algorithms to solve nonlinear equations (ongoing research project)
BSc (Computer Science – Special), TYT Totagamuwa, A Comparative Study on Weight Optimization of Neural Networks using Nature-inspired Algorithms with Cuckoo Search Algorithm (2013-14)
BSc (Computer Science – Special), WMKS Ilmini, Persons’ Personality Traits Recognition using Machine Learning Algorithms and Image Processing Techniques (2013-14)
BSc (Computer Science – Special), TMTM Perera, Multi Agent System for Doctor, Patient, Hospital and Pharmacy (2013-14)
MPhil (Computer Science), IDID Ariyasinghe, Designing and Developing Nature-Inspired Algorithms for Multi Objective Optimization Problems (ongoing research project)
BSc (Computer Science), MKA Ariyaratne, A Comparative Study on Nature-Inspired Algorithms with Firefly Algorithm (ongoing research project)
BSc (Computer Science), Janaki Wanigasooriya, Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms (2011-12)
MSc (Industrial Mathematics), Ranga P Abeysooriya, Genetic Optimization of Cut Order Planning in Apparel Manufacturing (2011)
BSc (Computer Science), KAG Udeshani, Lung Cancer Detection System Using Neural Networks and Image Processing Techniques (2010)
BSc (Mathematics), PCP Peiris, Review of the citations of the research paper: A variant of Newton’s method with accelerated third-order convergence (2010)
BSc (Computer Science), DVS Hettiarachchi, Mobile Navigation System (2009)
BSc (Mathematics), MDL Salgado, Degree Reduction of Bezier Curves (2009)
MSc (Industrial Mathematics), CP Amarajeewa, Optical Character Recognition (OCR) of Sinhala Characters by Feature Analysis Followed by Matrix Matching (2002).
MSc (Industrial Mathematics), PAS Pathiraja, Modelling Sinhala Characters using Cubic Rational Bézier Curves (2002).

Academic awards/Scholarships:
Presidential Award Winner (2000) – For the appreciation of Authorship of “A Variant of Newton’s Methods with Accelerated Third-Order Convergence.”
Received a Sri Lanka/ADB (Asian Development Bank) scholarship (2000) to study an MSc in Computer Science at Asian Institute of Technology, Thailand.
President’s fund, Sri Lanka awarded a scholarship of Rs. 1,500,000/- to study a PhD in a foreign university in 2004.
Brunel University, UK awarded a scholarship to study a PhD in 2005 .
Best poster – Science, ”MDL Salgado, S Weerakoon & TGI Fernando, Degree Reduction of Bezier Curves”, 50th Anniversary Academic Conference, University of Sri Jayewardenepura, 2009.

Referee work:
Reviewer of the Journal of Numerical Algorithms (Springer)
Reviewer of proceedings of annual sessions of Sri Lankan Association for Artificial Intelligence (SLAAI)
Judger of the best paper award, Proceedings of annual sessions of Sri Lankan Association for Artiﬁcial Intelligence (SLAAI).
Reviewer of proceedings of annual sessions of Information Technology Research Unit (ITRU), University of Moratuwa, Sri Lanka
Judger of the best paper award, Proceedings of annual sessions of Information Technology Research Unit (ITRU), University of Moratuwa, Sri Lanka.
Reviewer of the Journal of the Science Foundation of Sri Lanka (JNSF)
Address: Department of Computer Science, Faculty of Applied Science,
University of Sri Jayewardenepura,
Gangodawila,
Nugegoda,
Sri Lanka.

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Books by TGI Fernando

Ant colony optimization based simulation of 3d automatic hose/pipe routing (2009)

This thesis focuses on applying one of the rapidly growing non-deterministic optimization algorit... more This thesis focuses on applying one of the rapidly growing non-deterministic optimization algorithms, the ant colony algorithm, for simulating automatic hose/pipe routing with several conflicting objectives. Within the thesis, methods have been developed and applied to single objective hose routing, multi-objective hose routing and multi-hose routing. The use of simulation and optimization in engineering design has been widely applied in all fields of engineering as the computational capabilities of computers has increased and improved. As a result of this, the application of non-deterministic optimization techniques such as genetic algorithms, simulated annealing algorithms, ant colony algorithms, etc. has increased dramatically resulting in vast improvements in the design process. Initially, two versions of ant colony algorithms have been developed based on, respectively, a random network and a grid network for a single objective (minimizing the length of the hoses) and avoiding obstacles in the CAD model. While applying ant colony algorithms for the simulation of hose routing, two modifications have been proposed for reducing the size of the search space and avoiding the stagnation problem. Hose routing problems often consist of several conflicting or trade-off objectives. In classical approaches, in many cases, multiple objectives are aggregated into one single objective function and optimization is then treated as a single-objective optimization problem. In this thesis two versions of ant colony algorithms are presented for multihose routing with two conflicting objectives: minimizing the total length of the hoses and maximizing the total shared length (bundle length). In this case the two objectives are aggregated into a single objective. The current state-of-the-art approach for handling multi-objective design problems is to employ the concept of Pareto optimality. Within this thesis a new Pareto-based general purpose ant colony algorithm (PSACO) is proposed and applied to a multi-objective hose routing problem that consists of the following objectives: total length of the hoses between the start and the end locations, number of bends, and angles of bends. The proposed method is capable of handling any number of objectives and uses a single pheromone matrix for all the objectives. The domination concept is used for updating the pheromone matrix. Among the currently available multi-objective ant colony optimization (MOACO) algorithms, P-ACO generates very good solutions in the central part of the Pareto front and hence the proposed algorithm is compared with P-ACO. A new term is added to the random proportional rule of both of the algorithms (PSACO and P-ACO) to attract ants towards edges that make angles close to the pre-specified angles of bends. A refinement algorithm is also suggested for searching an acceptable solution after the completion of searching the entire search space. For all of the simulations, the STL format (tessellated format) for the obstacles is used in the algorithm instead of the original shapes of the obstacles. This STL format is passed to the C++ library RAPID for collision detection. As a result of using this format, the algorithms can handle freeform obstacles and the algorithms are not restricted to a particular software package.

TGI Fernando

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