Binary, unrelaxable and hidden constraints in blackbox optimization

2020 ◽  
Vol 48 (4) ◽  
pp. 467-471
Author(s):  
Charles Audet ◽  
Gilles Caporossi ◽  
Stéphane Jacquet
Keyword(s):  
Blackbox Optimization ◽  
Hidden Constraints

scholarly journals Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization

2020 ◽  
Vol 34 (06) ◽  
pp. 10044-10052 ◽  
Author(s):  
Syrine Belakaria ◽  
Aryan Deshwal ◽  
Nitthilan Kannappan Jayakodi ◽  
Janardhan Rao Doppa
Keyword(s):  
Optimization Problem ◽  
State Of The Art ◽  
Selection Method ◽  
Bayesian Optimization ◽  
Benchmark Problems ◽  
Multi Objective ◽  
Blackbox Optimization ◽  
Expensive Function ◽  
Area Overhead ◽  
Measure Of Uncertainty

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. The selection method of USeMO consists of solving a cheap MO optimization problem via surrogate models of the true functions to identify the most promising candidates and picking the best candidate based on a measure of uncertainty. We also provide theoretical analysis to characterize the efficacy of our approach. Our experiments on several synthetic and six diverse real-world benchmark problems show that USeMO consistently outperforms the state-of-the-art algorithms.

Surrogate Optimization of Computationally Expensive Black-Box Problems with Hidden Constraints

2019 ◽  
Vol 31 (4) ◽  
pp. 689-702 ◽  
Author(s):  
Juliane Müller ◽  
Marcus Day
Keyword(s):  
Objective Function ◽  
Optimization Problems ◽  
Black Box ◽  
Direct Search ◽  
Test Problems ◽  
Large Set ◽  
Mesh Adaptive Direct Search ◽  
Surrogate Optimization ◽  
Computationally Expensive ◽  
Hidden Constraints

We introduce the algorithm SHEBO (surrogate optimization of problems with hidden constraints and expensive black-box objectives), an efficient optimization algorithm that employs surrogate models to solve computationally expensive black-box simulation optimization problems that have hidden constraints. Hidden constraints are encountered when the objective function evaluation does not return a value for a parameter vector. These constraints are often encountered in optimization problems in which the objective function is computed by a black-box simulation code. SHEBO uses a combination of local and global search strategies together with an evaluability prediction function and a dynamically adjusted evaluability threshold to iteratively select new sample points. We compare the performance of our algorithm with that of the mesh-based algorithms mesh adaptive direct search (MADS, NOMAD [nonlinear optimization by mesh adaptive direct search] implementation) and implicit filtering and SNOBFIT (stable noisy optimization by branch and fit), which assigns artificial function values to points that violate the hidden constraints. Our numerical experiments for a large set of test problems with 2–30 dimensions and a 31-dimensional real-world application problem arising in combustion simulation show that SHEBO is an efficient solver that outperforms the other methods for many test problems.

Bio-Inspired Heuristic Network Configuration in Air Transportation System-of-Systems Design Optimization

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Ibrahim M. Chamseddine ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Systems Design ◽  
Air Transportation ◽  
Transportation System ◽  
System Of Systems ◽  
Network Configuration ◽  
Route Network ◽  
Blackbox Optimization ◽  
City Network ◽  
Optimization Loop

Previous work in air transportation system-of-systems (ATSoSs) design optimization considered integrated aircraft sizing, fleet allocation, and route network configuration. The associated nested multidisciplinary formulation posed a numerically challenging blackbox optimization problem; therefore, direct search methods with convergence properties were used to solve it. However, the complexity of the blackbox impedes greatly the solution of larger-scale problems, where the number of considered nodes in the route network is high. The research presented here adopts a rule-based route network design inspired by biological transfer principles. This bio-inspired approach decouples the network configuration problem from the optimization loop, leading to significant numerical simplifications. The usefulness of the bio-inspired approach is demonstrated by comparing its results to those obtained using the nested formulation for a 15 city network. We then consider introduction of new aircraft as well as a larger problem with 20 cities.

Linear equalities in blackbox optimization

2014 ◽  
Vol 61 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Charles Audet ◽  
Sébastien Le Digabel ◽  
Mathilde Peyrega
Keyword(s):  
Blackbox Optimization
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