Modular Optimization Framework for Mixed Expensive and Inexpensive Real-World Problems

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Abstract
A modular optimization framework is proposed that can deal with different problem characteristics encountered in real-world design problems. The common denominator of problems considered in this work is the computationally demanding objective function used to evaluate the designs, while some of the constraint functions can be inexpensive. The framework uses radial basis functions as surrogates for the computationally expensive objectives while inexpensive functions can directly be used for searching promising solutions on the surrogates. The framework is adjustable for the number of continuous design parameters, the number of constraints, the number of solutions that can be evaluated in parallel, and different strategies can be selected on how to deal with the computationally inexpensive functions. We propose guidelines on how to set up the modular framework depending on the problem characteristics one has to deal with. To verify the working of the optimization framework, two different real-world ship design optimization problems have been optimized with different characteristics such as the number of constraints and how expensive they are. The results show that it is beneficial to directly exploit the inexpensive functions when possible and that parameterization of the problem is the most important step of the process.

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