Multiple Objective Evolution Strategies (MOES): A User's Guide to Running the Software

Abstract

A user s guide for the parallel Multiple Objective Evolution Strategies (MOES) software package is presented. MOES employs a sophisticated self-adaptive evolutionary algorithm known as Evolution Strategies. The software can perform single objective optimization (with and without constraints) as well as multiple objective optimization using a fitness function based on Pareto dominance. The novel multiple-objective fitness function is computed using the concept of efficiency from Data Envelopment Analysis (DEA), a specialized application of linear programming. MOES is unique in combining a very flexible self-adaptive algorithm with a novel multiple-objective algorithm to compute Pareto fitness, all within a package that has been efficiently parallelized.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2014
Accession Number
ADA612711

Entities

People

  • Anthony Yau
  • James Lill

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Algorithms
  • Application Software
  • Computer Programming
  • Computer Programs
  • Convergence
  • Debugging
  • Demographic Cohorts
  • Efficiency
  • Evolutionary Algorithms
  • Insensitive Explosives
  • Linear Programming
  • Materials
  • Military Research
  • Optimization
  • Personal Information Managers
  • Standards

Fields of Study

  • Computer science

Readers

  • Computational Fluid Dynamics (CFD)
  • Instructional Design and Training Evaluation.
  • Operations Research