Abstract
In the past few years, bio-inspired optimization algorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms to better understand and measure how well they find the best tuning parameters for a Dynamic Sliding Mode Control for integrating systems with an inverse response and dead time. The comparison includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms. It shows how they can improve the performance of the controller by looking for the best tuning parameter solutions. The parameters of each algorithm affect the searching mechanism in different ways, and these effects were tested in two simulated systems. Ant colony optimization is much better than other algorithms at finding the best answers to our problems.
Published Version
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