ACO with GA operators for solving University Class Scheduling Problem with flexible preferences

University Class Scheduling Problem (UCSP) is very hard constraint satisfaction problem (CSP). To solve UCSP, we introduce a novel Ant Colony Optimization (ACO) with Genetic Algorithm Operators (GAO) (ACOGAO) method. ACO is well known for combinatorial optimization and finds optimal sequence of solution points. On the other hand, GA is a population based adaptive heuristic method that starts with a sample set of solutions and then evolves into a set of optimal solutions. In the proposed ACOGAO method, ACO is used to provide UCSP´s solution and GAO such as selection and mutation is employed to improve UCSP´s solution. The experiment is conducted in academic class scheduling for Computer Science and Engineering (CSE) department of Khulna University of Engineering & Technology (KUET). The experimental results demonstrate that the proposed algorithm yields an efficient solution with an optimal satisfaction of course scheduling for instructors and class scheduling arrangements.