Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Constraint Handling Approach with Guaranteed Feasibility for Surrogate
  Based Optimization   Gradient-free optimization methods, such as surrogate based optimization
(SBO) methods, and genetic (GAs), or evolutionary (EAs) algorithms have gained
popularity in the field of constrained optimization of expensive black-box
functions. However, constraint-handling methods, by both classes of solvers, do
not usually guarantee strictly feasible candidates during optimization. This
can become an issue in applied engineering problems where design variables must
remain feasible for simulations to not fail. We propose a constraint-handling
method for computationally inexpensive constraint functions which guarantees
strictly feasible candidates when using a surrogate-based optimizer. We compare
our method to other SBO, GA/EA and gradient-based algorithms on two (relatively
simple and relatively hard) analytical test functions, and an applied
fully-resolved Computational Fluid Dynamics (CFD) problem concerned with
optimization of an undulatory swimming of a fish-like body, and show that the
proposed algorithm shows favorable results while guaranteeing feasible
candidates.

24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org