Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game

Peng Huo, Chi Keung Simon Shiu, Haibo Wang, Ben Niu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)


Particle swarm optimization (PSO) is similar to genetic algorithm (GA) but employs different strategies and computational effort. Strategic defense military games require a high degree of coordination among the characters and thus are suitable to test the performance of algorithms. In this paper, we design a scenario of tower defense game and compare the performance of PSO and GA in terms of the damage value (fitness) and the convergence speed. The comparative analysis shows the similar optimum cannon placement is obtained using PSO and GA with similar effectiveness. In addition, the results of execution time (> 80 seconds) indicate that the single implement of PSO or GA is unsatisfied for real time strategy (RTS) games.
Original languageEnglish
Title of host publication5th International Conference on Natural Computation, ICNC 2009
Number of pages6
Publication statusPublished - 1 Dec 2009
Event5th International Conference on Natural Computation, ICNC 2009 - Tianjian, China
Duration: 14 Aug 200916 Aug 2009


Conference5th International Conference on Natural Computation, ICNC 2009

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Software

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