The Genetic-Algorithm-Based Normal Boundary Intersection (GANBI) Method; An Efficient Approach to Pareto Multiobjective Optimization for Engineering Design

Abstract

A new method for developing tradeoffs in the engineering of complex systems is described. The Genetic-Algorithm-Based Normal Boundary Intersection (GANBI) method serves as a preprocessor for conventional genetic-algorithm-based Pareto optimization solvers. The algorithm is based on applying the normal boundary intersection approach of Pareto optimization to genetic solvers. The approach is shown to provide rapid convergence and to provide a better estimate of the Pareto set than existing state-of-the-art methods. A description of Pareto optimization methods for engineering design is included to put the new method in the context of existing solution approaches. The algorithm for the GANBI method is derived and detailed in the report, and numerical examples showing its efficiency in solving an academic problem are presented. The report concludes with an example of how the GANBI method has been used to make tradeoff decisions in the design of large-scale distributed undersea sensor networks.

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Document Details

Document Type
Technical Report
Publication Date
May 15, 2006
Accession Number
ADA455270

Entities

People

  • Thomas Wettergren

Organizations

  • Naval Undersea Warfare Center

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Complex Systems
  • Computational Complexity
  • Computer Science
  • Convergence
  • Detection
  • Detectors
  • Engineering
  • Equations
  • False Alarms
  • Genetic Algorithms
  • Multiobjective Optimization
  • Networks
  • Optimization
  • Sensor Networks
  • Systems Engineering

Readers

  • Operations Research
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Biotechnology