Multi-Objective Optimization of Mixed Variable, Stochastic Systems Using Single-Objective Formulations

Abstract

Two new algorithms are presented for multi-objective optimization of mixed-variable, stochastic systems. Both are based off of prior algorithms, but combine those pre-exsting algorithms with several other methods, to include single-objective formulations, surrogates, n-dimensional visualizations, aspiration an reservation levels, and direct search methods. Results are shown for a test set of 13 problems, ranging from 2 to 8 objectives, and including non-convex, mixed-variable, and discontinuous problems.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2008
Accession Number
ADA488130

Entities

People

  • Todd J. Paciencia

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Programming
  • Counting Methods
  • Evolutionary Algorithms
  • Experimental Design
  • Genetic Algorithms
  • Information Processing
  • Information Science
  • Multiobjective Optimization
  • Neural Networks
  • Operations Research
  • Optimization
  • Statistical Algorithms
  • Test Sets
  • Two Dimensional
  • Visualizations

Readers

  • Operations Research