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Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics.

Zne-Jung Lee1, Shun-Feng Su, Chou-Yuan Lee

  • 1Dept. of Inf. Manage., Kang-Ning Junior Coll., Taipei, Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 2, 2008
PubMed
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This study introduces a novel genetic algorithm (GA) with greedy eugenics to optimize weapon-target assignment (WTA) problems. The enhanced GA improves performance by creating locally optimal offspring, outperforming existing search algorithms.

Area of Science:

  • Operations Research
  • Computer Science
  • Artificial Intelligence

Background:

  • The weapon-target assignment (WTA) problem is critical for minimizing own-force asset damage.
  • Genetic algorithms (GAs) are commonly employed for complex optimization tasks like WTA.

Purpose of the Study:

  • To propose a novel genetic algorithm (GA) with greedy eugenics for solving the general WTA problem.
  • To enhance GA performance through a greedy reformation scheme for locally optimal offspring.

Main Methods:

  • Development of a novel GA incorporating a greedy eugenics strategy.
  • Application and simulation of the proposed algorithm on general WTA problems.
  • Comparative analysis against existing search algorithms.

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Main Results:

  • The proposed GA with greedy eugenics demonstrated superior performance in WTA problem-solving.
  • Simulations confirmed the algorithm's effectiveness on tested problems.
  • The algorithm achieved better results compared to other existing search methods.

Conclusions:

  • The novel GA with greedy eugenics is a highly effective approach for the general WTA problem.
  • The greedy reformation scheme significantly enhances GA performance by generating better offspring.
  • This algorithm represents an advancement in solving complex optimization problems in defense applications.