Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system
Chugh, T., Sindhya, K., Miettinen, K., Jin, Y., Kratky, T., & Makkonen, P. (2017). Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system. In 2017 IEEE Congress on Evolutionary Computation (CEC) (pp. 1541-1548). IEEE. https://doi.org/10.1109/CEC.2017.7969486
© 2017 IEEE. This is a final draft version of an article whose final and definitive form has been published by IEEE. Published in this repository with the kind permission of the publisher.
We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. From the set of nondominated solutions generated by K-RVEA, a decision maker having substance knowledge selected the final one based on his preferences. The final selected solution has better objective function values compared to the baseline solution of the initial design. A comparison of solutions with K-RVEA and RVEA (which does not use surrogates) is also performed to show the potential of using surrogates. ...
Parent publication ISBN978-1-5090-4601-0
ConferenceIEEE Congress on Evolutionary Computation
Is part of publication2017 IEEE Congress on Evolutionary Computation (CEC)
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
Surrogate-Assisted Evolutionary Optimization of Large Problems Chugh, Tinkle; Sun, Chaoli; Wang, Handing; Jin, Yaochu (Springer, 2020)This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is ...
Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies Chugh, Tinkle; Allmendinger, Richard; Ojalehto, Vesa; Miettinen, Kaisa (Association for Computing Machinery (ACM), 2018)We consider multiobjective optimization problems where objective functions have different (or heterogeneous) evaluation times or latencies. This is of great relevance for (computationally) expensive multiobjective optimization ...
A Data-Driven Surrogate-Assisted Evolutionary Algorithm Applied to a Many-Objective Blast Furnace Optimization Problem Chugh, Tinkle; Chakraborti, Nirupam; Sindhya, Karthik; Jin, Yaochu (Taylor & Francis Inc., 2017)A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives ...
On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization Chugh, Tinkle; Sindhya, Karthik; Miettinen, Kaisa; Hakanen, Jussi; Jin, Yaochu (Springer International Publishing, 2016)Surrogate-assisted evolutionary multiobjective optimization algorithms are often used to solve computationally expensive problems. But their efficacy on handling constrained optimization problems having more than three ...