Abstract
Generally speaking, the couples of simulator-optimizer are very common combination in optimizing the systems in science and engineering fields; but when the simulation process is an expensive one, then each fitness evaluation of the optimizer can take several hours even days, which makes the mentioned process impractical to run in given time budget. In this paper, we replace the solar chimney power plant (SCPP) simulator with a bi-objective meta-model which is created by Genetic Programming (GP) and we compared the created model with neural network and regression models to be sure it is accurate enough. Then, we have utilized a genetic algorithm based multi-objective algorithm to solve the created bi-objective optimization problem resulted from the meta-modeling phase. After finding optimal Pareto-front (PF), we use a GP-based innovization technique to discover engineering design knowledge and rules for the designed optimal power plant. The created models have been validated with results of the 4 corresponding simulator, a promising error rate (\({<}\)4%) has been obtained. This work can be evaluated as a successful energy application of GP-based meta-modeling, evolutionary multi-objective optimization, and GP-based innovization.
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Azimlu, F., Rahnamayan, S., Makrehchi, M., Karimipour-Fard, P. (2019). Designing Solar Chimney Power Plant Using Meta-modeling, Multi-objective Optimization, and Innovization. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_58
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