Multiple UAV Task Allocation for an Electronic Warfare Mission Comparing Genetic Algorithms and Simulated Annealing (Preprint)

Abstract

This paper compares two algorithms applied to the task allocation of multiple Unmanned Aerial Vehicles (UAVs) for an electronic warfare mission. The electronic warfare mission scenario is discussed and a review of both the genetic algorithm and simulated annealing algorithm is given. The encoding of the problem and the functions and operations needed to implement each algorithm is outlined and compared. The algorithms were implemented and tested in Matlab. A discussion of the performance analysis for the time to convergence and quality of solutions in a fixed period of time is given.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2006
Accession Number
ADA462016

Entities

People

  • Brian Stolarik
  • Marjorie A. Darrah
  • William Niland

Tags

Communities of Interest

  • Air Platforms
  • Electronic Warfare
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Algorithms
  • Annealing
  • Battle Damage Assessment
  • Coding
  • Convergence
  • Damage Assessment
  • Electronic Warfare
  • Genetic Algorithms
  • Government Procurement
  • Governments
  • Information Operations
  • Military Research
  • Vehicles
  • Warfare

Fields of Study

  • Engineering

Readers

  • Operations Research
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Autonomy
  • Autonomy - UAVs
  • Biotechnology
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems