Sudip Kumar Sahana

Work place: Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India



Research Interests: Computing Platform, Network Architecture, Computer Architecture and Organization, Autonomic Computing, Artificial Intelligence


Sudip Kumar Sahana, male, was born in Purulia West Bengal, India on 8th October, 1976. He received the B.E degree in Computer Technology from Nagpur University, India in 2001, the M.Tech. degree in Computer Science in 2006 and Ph.D in Engineering in 2013 from the B.I.T (Mesra), Ranchi, India.

He is currently working as Associate Professor in the Department of Computer Science and Engineering, B.I.T(Mesra), Ranchi, India.

His research and teaching interests include soft computing, grid computing, network traffic management and artificial intelligence. He is author of number of research papers in the field of Computer Science.

Author Articles
Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem

By Sudip Kumar Sahana Mohammad AL-Fayoumi Prabhat Kumar Mahanti

DOI:, Pub. Date: 8 Apr. 2016

It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO) algorithm which is based on Ant Colony System (ACS) with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO) shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.

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Ant Colony Optimization for Train Scheduling: An Analysis

By Sudip Kumar Sahana Aruna Jain Prabhat Kumar Mahanti

DOI:, Pub. Date: 8 Jan. 2014

This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.

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Other Articles