Md. Taief Imam

Work place: CoKreates Limited, Kawran Bazar, Dhaka 1216, Bangladesh



Research Interests: Software Engineering, Computational Engineering, Computational Science and Engineering


Md Taief Imam has received his B.Sc. degree in Computer Science and Engineering from Ahsanullah University of Science and Technology, Bangladesh, in 2019. He’s currently working as a Software Quality Assurance Engineer at Cokreates Ltd. He’s involved with Bangladesh e-Government ERP Project (GRP). This is a project of Planning division, ICT Division, High-Tech Park, BCC, a2i, BUET. He had received an appreciation letter as an SQA Engineer from Bangladesh Computer Council last December to work actively in the QA team. His current goal is to represent himself as a QA professional at an ever growing and challenging corporate environment to excel through work, dedication and quick learning.

Author Articles
A Multi-Objective Optimization Approach for Solving AUST Classtimetable Problem Considering Hard and Soft Constraints

By Md Shahriar Mahbub Shihab Shahriar Ahmed Kazi Irtiza Ali Md. Taief Imam

DOI:, Pub. Date: 8 Oct. 2020

Preparing a class timetable or routine is a difficult task because it requires an iterative trial and error method to handle all the constraints. Moreover, it has to be beneficial both for the students and teachers. Therefore, the problem becomes a multi-objective optimization problem with a good number of constraints. There are two types of constraints: hard and soft constraint. As the problem is an NP-hard problem, population based multi-objective optimization algorithms (multi-objective evolutionary algorithm) is a good choice for solving the problem. There are well established hard constraints handling techniques for multi-objective evolutionary algorithms, however, the technique is not enough to solve the problem efficiently. In the paper, a smart initialization technique is proposed to generate fewer constraints violated solutions in the initial phase of the algorithm so that it can find feasible solutions quickly. An experimental analysis supports the assumption. Moreover, there are no well-known techniques available for handling soft constraints. A new soft constraints handing technique is proposed. Experimental results show a significant improvement can be achieved. Finally, proposed combined approach integrates smart initialization and soft constraints handling techniques. Better results are reported when comparing with a standard algorithm.

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