Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

Imam Ahmad Ashari, Much Aziz Muslim, Alamsyah Alamsyah

Abstract


Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization algorithm in solving the case of course scheduling.


Keywords


Course scheduling, Genetic algorithm, Ant colony optimization algorithm, Metaheuristic algorithm, Performance.

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References


Nugraha, D., & Kosala, R. 2014. A Comparative Study of Evolutionary Algorithms for School Scheduling Problem. Journal Of Theoretical & Applied Information Technology, 67(3).

Babaei, H., Karimpour, J., & Hadidi, A. 2015. A survey of Approaches for University Course Timetabling Problem. Computers & Industrial Engineering, 86: 43-59.

Alamsyah, Wardoyo, R. 2004. Optimalisasi Penjadwalan Multi Constraint menggunakan Logika Fuzzy = Multi Constraint Scheduling Optimization Using Fuzzy Logic. Sains dan Sibernatika, 17.

Reddy, S. S., & Bijwe, P. R. 2016. Efficiency Improvements in Meta-heuristic Algorithms to Solve the Optimal Power Flow Problem. International Journal of Electrical Power & Energy Systems, 82, 288-302.

Deb, K. (2001). Multi-objective Optimization using Evolutionary Algorithms (Vol. 16). John Wiley & Sons.

Osman, M. S., Abo-Sinna, M. A., & Mousa, A. A. 2004. A Solution to the Optimal Power Flow Using Genetic Algorithm. Applied mathematics and computation, 155(2), 391-405.

Tiwari, P. K., & Vidyarthi, D. P. 2016. Improved Auto Control Ant Colony Optimization using Lazy Ant Approach for Grid Scheduling Problem. Future Generation Computer Systems, 60: 78-89.

Yuryevich, J., & Wong, K. P. 1999. Evolutionary Programming Based Optimal Power Flow Algorithm. IEEE Transactions on Power Systems, 14(4), 1245-1250.

Abido, M. A. 2002. Optimal Power Flow using Particle Swarm Optimization. International Journal of Electrical Power & Energy Systems, 24(7), 563-571.

El Ela, A. A., Abido, M. A., & Spea, S. R. 2010. Optimal Power Flow using Differential Evolution Algorithm. Electric Power Systems Research, 80(7), 878-885.

Abido, M. A. 2002. Optimal Power Flow using Tabu Search Algorithm. Electric Power Components and Systems, 30(5), 469-483.

Bhattacharya, A., & Chattopadhyay, P. K. 2011. Application of Biogeography-based Optimisation to Solve Different Optimal Power Flow Problems. IET generation, transmission & distribution, 5(1), 70-80.

Roa-Sepulveda, C. A., & Pavez-Lazo, B. J. 2003. A Solution to the Optimal Power Flow using Simulated Annealing. International journal of electrical power & energy systems, 25(1), 47-57.

Squillero, G., & Tonda, A. 2016. Divergence of Character and Premature Convergence: A Survey of Methodologies for Promoting Diversity in Evolutionary Optimization. Information Sciences, 329: 782-799.

Hoseini, P., & Shayesteh, M. G. 2013. Efficient Contrast Enhancement of Images using Hybrid Ant Colony Optimisation, Genetic Algorithm, and Simulated Annealing. Digital Signal Processing, 23(3), 879-893.

Jiang, Z., Zhou, M., Tong, J., Jiang, H., Yang, Y., Wang, A., & You, Z. 2015. Comparing An Ant Colony Algorithm with a Genetic Algorithm for Replugging Tour Planning of Seedling Transplanter. Computers and Electronics in Agriculture, 113, 225-233.

Aguilar-Rivera, R., Valenzuela-Rendn, M., & Rodrguez-Ortiz, J. J. 2015. Genetic Algorithms and Darwinian Approaches in Financial Applications: A survey. Expert Systems with Applications, 42(21), 7684-7697.

Pressman, Roger S. 2002. Rekayasa Perangkat Lunak. Yogyakarta: Andi.

Hong, S. S., Lee, W., & Han, M. M. 2015. The Feature Selection Method based on Genetic Algorithm for Efficient of Text Clustering and Text Classification. International Journal of Advances in Soft Computing & Its Applications, 7(1).

Sitarz, P., & Powa?ka, B. 2016. Modal Parameters Estimation using Ant Colony Optimisation Algorithm. Mechanical Systems and Signal Processing, 76, 531-554.




DOI: https://doi.org/10.15294/sji.v3i2.7911

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