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

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

doi:10.1109/ICNC.2009.552

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 proceeding › Conference article published in proceeding or book › Academic research › peer-review

12 Citations (Scopus)

Abstract

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 language	English
Title of host publication	5th International Conference on Natural Computation, ICNC 2009
Pages	387-392
Number of pages	6
Volume	5
DOIs	https://doi.org/10.1109/ICNC.2009.552
Publication status	Published - 1 Dec 2009
Event	5th International Conference on Natural Computation, ICNC 2009 - Tianjian, China Duration: 14 Aug 2009 → 16 Aug 2009

Conference

Conference	5th International Conference on Natural Computation, ICNC 2009
Country/Territory	China
City	Tianjian
Period	14/08/09 → 16/08/09

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Software

More information

10.1109/ICNC.2009.552

Cite this

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title = "Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game",

abstract = "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.",

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Huo, P, Shiu, CKS, Wang, H & Niu, B 2009, Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game. in 5th International Conference on Natural Computation, ICNC 2009. vol. 5, 5364640, pp. 387-392, 5th International Conference on Natural Computation, ICNC 2009, Tianjian, China, 14/08/09. https://doi.org/10.1109/ICNC.2009.552

Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game. / Huo, Peng; Shiu, Chi Keung Simon; Wang, Haibo; Niu, Ben.

5th International Conference on Natural Computation, ICNC 2009. Vol. 5 2009. p. 387-392 5364640.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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

Abstract

Conference

ASJC Scopus subject areas

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