Pintu Chnadra Shill

Work place: Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology Khulna, Bangladesh



Research Interests: Bioinformatics, Artificial Intelligence, Evolutionary Computation, Neural Networks, Logic Calculi, Logic Circuit Theory


Pintu Chandra Shill received the B.Sc. degree in Computer Science Engineering (CSE) from Khulna University of Engineering and Technology (KUET), Bangladesh in 2003 and M.Sc. degree in Computer Engineering from Politecnico di Milano, Italy in 2008. He joined as a lecturer at the Department of CSE, KUET in 2004 and currently he is serving as an Assistant Professor. He has published several research papers in some reputed Journal and Conference. His research interest includes evolutionary computation, fuzzy logic, bioinformatics and artificial neural networks.

Author Articles
Producer-Scrounger Method to Solve Traveling Salesman Problem

By M. A. H. Akhand Pintu Chnadra Shill Forhad Hossain A. B. M. Junaed Kazuyuki Murase

DOI:, Pub. Date: 8 Feb. 2015

Algorithms inspired from natural phenomena are seem to be efficient to solve various optimization problems. This paper investigates a new technique inspiring from the animal group living behavior to solve traveling salesman problem (TSP), the most popular combinatorial optimization problem. The proposed producer-scrounger method (PSM) models roles and interactions of three types of animal group members: producer, scrounger and dispersed. PSM considers a producer having the best tour, few dispersed members having worse tours and scroungers. In each iteration, the producer scans for better tour, scroungers explore new tours while moving toward producer’s tour; and dispersed members randomly checks new tours. For producer’s scanning, PSM randomly selects a city from the producer’s tour and rearranges its connection with several near cities for better tours. Swap operator and swap sequence based operation is employed in PSM to update a scrounger towards the producer. The proposed PSM has been tested on a large number of benchmark TSPs and outcomes compared to genetic algorithm and ant colony optimization. Experimental results revealed that proposed PSM is a good technique to solve TSP providing the best tours in most of the TSPs.

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