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	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

@inproceedings{2b4f50d477a04cb094fd47d55c51d93b,

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.",

author = "Peng Huo and Shiu, {Chi Keung Simon} and Haibo Wang and Ben Niu",

year = "2009",

month = dec,

day = "1",

doi = "10.1109/ICNC.2009.552",

language = "English",

isbn = "9780769537368",

volume = "5",

pages = "387--392",

booktitle = "5th International Conference on Natural Computation, ICNC 2009",

note = "5th International Conference on Natural Computation, ICNC 2009 ; Conference date: 14-08-2009 Through 16-08-2009",

}

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

TY - GEN

T1 - Application and comparison of particle swarm optimization and genetic algorithm in strategy defense game

AU - Huo, Peng

AU - Shiu, Chi Keung Simon

AU - Wang, Haibo

AU - Niu, Ben

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=77950563493&partnerID=8YFLogxK

U2 - 10.1109/ICNC.2009.552

DO - 10.1109/ICNC.2009.552

M3 - Conference article published in proceeding or book

SN - 9780769537368

VL - 5

SP - 387

EP - 392

BT - 5th International Conference on Natural Computation, ICNC 2009

T2 - 5th International Conference on Natural Computation, ICNC 2009

Y2 - 14 August 2009 through 16 August 2009

ER -