This chapter proposes a hybrid approach by combining a Euclidian distance (EU) based genetic algorithm (GA) and particle swarm optimization (PSO) method. The performance of the hybrid algorithm is illustrated using four test functions. Proportional integral derivative (PID) controllers have been widely used in industrial systems such as chemical process, biomedical process, and in the main steam temperature control system of the thermal power plant. Very often, it is difficult to achieve an optimal PID gain without prior expert knowledge, since the gain of the PID controller has to be manually tuned by a trial and error approach. Using the hybrid EU–GA–PSO approach, global and local solutions could be simultaneously found for optimal tuning of the controller parameters.
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Kim, D.H., Abraham, A., Hirota, K. (2007). Hybrid Genetic: Particle Swarm Optimization Algorithm. In: Abraham, A., Grosan, C., Ishibuchi, H. (eds) Hybrid Evolutionary Algorithms. Studies in Computational Intelligence, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73297-6_7
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DOI: https://doi.org/10.1007/978-3-540-73297-6_7
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