Multi Objective Optimization Problems, Swarm Intelligence
B.Sc. Degree Programme
Course Coordination:
CSU4301 – Object Oriented Programming
CSU5308 – Artificial Intelligence
CSU5311 – Computer Graphics
1. Ariyasingha IDID, Fernando TGI (2015). Performance analysis of the multi-objective
ant colony optimization algorithms for the traveling salesman problem. Swarm and Evolutionary
Computation, 23: pp. 11-26.
2. Ariyasingha IDID, Fernando TGI (2015). A Performance Study for the Multi-objective
Ant Colony Optimization Algorithms on the Job Shop Scheduling Problem. International
Journal of Computer Applications: Published by Foundation of Computer Science (FCS),
NY, USA. 132(14): pp. 1-8.
1. AriyasinghaIDID and Fernando TGI (2017). Random Weight-based Ant Colony OptimisationAlgorithm
for the Multi-objective OptimisationProblems. Int. J. Swarm Intelligence, Vol. 3, No. 1: pp.77–100.
1. AriyasinghaIDID, Fernando TGI (2016). A Modified Pareto Strength Ant Colony Optimization
Algorithm for the Multi-objective Optimization Problems. 2016 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), pp. 1-6.
2. Ariyasingha IDID, Fernando TGI (2014). Analysis the Performances of the Multiobjective
Ant Colony Optimization Algorithms. Proceedings of the 70th Annual Sessions
of the Sri Lanka Association for the Advancement of Science, 503/E1. pp. 54.
President’s Award for Scientific Publication 2015