A Survey on Direct Search Methods for Blackbox Optimization and Their Applications

Charles Audet ()
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Charles Audet: École Polytechnique de Montréal and GERAD, Département de Mathématiques et de Génie Industriel

A chapter in Mathematics Without Boundaries, 2014, pp 31-56 from Springer

Abstract: Abstract Blackbox optimization typically arises when the functions defining the objective and constraints of an optimization problem are computed through a computer simulation. The blackbox is expensive to compute, can have limited precision and can be contaminated with numerical noise. It may also fail to return a valid output, even when the input appears acceptable. Launching twice the simulation from the same input may produce different outputs. These unreliable properties are frequently encountered when dealing with real optimization problems. The term blackbox is used to indicate that the internal structure of the target problem, such as derivatives or their approximations, cannot be exploited as it may be unknown, hidden, unreliable, or inexistent. There are situations where some structure such as bounds or linear constraints may be exploited and in some cases a surrogate of the problem is supplied or a model may be constructed and trusted. This chapter surveys algorithms for this class of problems, including a supporting convergence analysis based on the nonsmooth calculus. The chapter also lists numerous published applications of these methods to real optimization problems.

Keywords: Blackbox optimization; Direct search methods; Derivative-free optimization; Surrogate models; Nonsmooth analysis; Applications (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/978-1-4939-1124-0_2

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