Authors: Mohammad Tabatabaei Jussi Hakanen Markus Hartikainen Kaisa Miettinen Karthik Sindhya
Publish Date: 2015/03/10
Volume: 52, Issue: 1, Pages: 1-25
Abstract
Computationally expensive multiobjective optimization problems arise eg in many engineering applications where several conflicting objectives are to be optimized simultaneously while satisfying constraints In many cases the lack of explicit mathematical formulas of the objectives and constraints may necessitate conducting computationally expensive and timeconsuming experiments and/or simulations As another challenge these problems may have either convex or nonconvex or even disconnected Pareto frontier consisting of Pareto optimal solutions Because of the existence of many such solutions typically a decision maker is required to select the most preferred one In order to deal with the high computational cost surrogatebased methods are commonly used in the literature This paper surveys surrogatebased methods proposed in the literature where the methods are independent of the underlying optimization algorithm and mitigate the computational burden to capture different types of Pareto frontiers The methods considered are classified discussed and then compared These methods are divided into two frameworks the sequential and the adaptive frameworks Based on the comparison we recommend the adaptive framework to tackle the aforementioned challenges
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