Logo

Optimization of Units Movement in Turn-Based Strategy Game

Purba, Kristo Radion and Liliana, and PRANATA, JOHAN (2016) Optimization of Units Movement in Turn-Based Strategy Game. International Journal of Industrial Research and Applied Engineering, 1 (1). pp. 33-37. ISSN 2407-7259

[img] PDF
Download (262Kb)

    Abstract

    Each game has an artificial intelligence that is used to fight the player, which will provide more challenge. But in some strategy games, unit movements are usually done using simple considerations. For example the rest of unit lives, unit strength, and so forth. In this study, a turn based strategy game is designed using genetic algorithm to control the movement of the enemy armies. In each turn, the enemy will move based on the potential level of produced damage to and from the opponent, the distance between the units, and the distance to the opponent�s building. The genetic algorithm�s chromosome for each unit contains the following information: the position where the unit will move, who is the target, and the distance to the armies� centroid. Distance to centroid (midpoint) is used to force the units to remain in the set. The genetic algorithm process is used to control when and where the units will move or attack. From the test results, the genetic algorithm can create a more powerful enemy than the randomly moving enemy because it creates a higher winning chance of enemy units and acts more efficiently, in terms of the usage of money, the damage produced to the opponent, and the received damage.

    Item Type: Article
    Additional Information: Jurnal internasional baru belum terindex, sehingga poin dan deklarasi disetarakan dengan jurnal nasional terakreditasi
    Uncontrolled Keywords: Artificial Intelligence, Genetic Algorithm, Turn-based strategy game, Units Movement
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Divisions: Faculty of Industrial Technology > Informatics Engineering Department
    Depositing User: Admin
    Date Deposited: 21 Nov 2016 20:16
    Last Modified: 17 Jul 2019 12:09
    URI: http://repository.petra.ac.id/id/eprint/17702

    Actions (login required)

    View Item