Hybrid Tuning of an Evolutionary Algorithm for Sensor Allocation

Abstract

The application of evolutionary algorithms to the optimization of sensor allocation given different target configurations requires the tuning of parameters affecting the robustness and run time of the algorithm. In this context, parameter settings in evolutionary algorithms are usually set through empirical testing or rules of thumb that do not always provide optimal results within time constraints. Design of experiments (DOE) is a methodology that provides some principled guidance on parameter settings in a constrained experiment environment but relies itself on a final inductive step for optimization. This paper describes a sensor allocation tool developed for intelligence surveillance and reconnaissance (ISR) in the maritime domain and introduces a hybrid methodology based on DOE and machine learning techniques that enables the tuning of an embedded particle swarm optimization (PSO) algorithm for different scenarios.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2011
Accession Number
ADA638071

Entities

People

  • Ian Will
  • Myriam Abramson
  • Ranjeev Mittu

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • C4I
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Dimensionality Reduction
  • Evolutionary Algorithms
  • Experimental Design
  • Guidance
  • Information Science
  • Kernel Functions
  • Learning
  • Machine Learning
  • Multiagent Systems
  • Optimization
  • Particle Swarm Optimization
  • Particles
  • Probability
  • Supervised Machine Learning
  • Unmanned Aerial Vehicles

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design

Technology Areas

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