Friday, 11:30 - 11:55 h, Room: H 3003A

 

Joshua Griffin
A parallel hybrid derivative-free SAS procedure for MINLP

Coauthor: Steven Gardner

 

Abstract:
We present a new parallel derivative-free SAS procedure for mixed-integer nonlinear black-box optimization. The solver is motivated by recent work on the EAGLS (Evolutionary Algorithms Guiding Local Search) algorithm developed for simulation-based groundwater optimization problems. The SAS procedure makes minimal assumptions on the structure of the nonlinear objective/constraint functions; they may be discontinuous, noisy, and expensive to evaluate. Integer variables are handled by running multiple genetic algorithms concurrently. In addition to crossover and mutation, a "growth step'' permits selected members of the population (based on fitness and diversity) to benefit from local optimization over the real variables. For local search algorithms normally limited to real variables, this provides a simple framework for supporting integer variables that fits naturally in a parallel context. Load imbalance is exploited by both global and local algorithms sharing evaluation threads running across multiple processors. Unique evaluations are cached. Linear constraints are handled explicitly using tangent search directions and the SAS/OR OPTLP procedure.

 

Talk 3 of the invited session Fri.1.H 3003A
"MINLP and constrained optimization without derivatives" [...]
Cluster 6
"Derivative-free & simulation-based optimization" [...]

 

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