Artificial Intelligence Applications in Engineering (MUH-920), week 6 of 14: interactive lab
Prof. Dr. Utku Kose, Süleyman Demirel University
Part A shows a particle swarm on a two-dimensional landscape with controls for inertia, cognitive and social coefficients [1, 2]. Part B tunes the PID speed controller of a DC motor with the parameters of the Control Tutorials for MATLAB and Simulink and a 24-volt limit, and compares the swarm's gains with a reference tuning [3]. Part C runs an ant colony on a drilling sequence and shows pheromone trails forming [4].
Six questions with instant feedback. Rate your confidence before checking each answer.
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[1] Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95, International Conference on Neural Networks, Vol. 4 (pp. 1942-1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
[2] Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In 1998 IEEE International Conference on Evolutionary Computation Proceedings (pp. 69-73). IEEE. https://doi.org/10.1109/ICEC.1998.699146
[3] University of Michigan, Carnegie Mellon University, & University of Detroit Mercy (2026). Control Tutorials for MATLAB and Simulink: DC motor speed, system modeling. https://ctms.engin.umich.edu
[4] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1), 29-41. https://doi.org/10.1109/3477.484436
[5] Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. https://doi.org/10.1007/s10898-007-9149-x
[6] Sörensen, K. (2015). Metaheuristics: The metaphor exposed. International Transactions in Operational Research, 22(1), 3-18. https://doi.org/10.1111/itor.12001