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Exploring Metaheuristic Algorithms for Enhanced Game Map Generation in Procedural Content Generation

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  • Sana Alyaseri

    (Whitecliffe College, New Zealand)

  • Andy Connor

    (Auckland University of Technology, New Zealand)

  • Roopak Sinha

    (Deakin University, Australia)

Abstract

This study examines the performance of genetic algorithms, the particle swarm optimization (PSO) algorithm, and the artificial bee colony (ABC) algorithm in procedural content generation for game map layouts. A series of experiments evaluated each algorithm's efficiency based on convergence speed, content quality, and overall map structure. The results showed that the genetic algorithms with tournament selection outperformed the PSO and the ABC algorithms in generating high-quality maps, though the PSO and the ABC algorithms demonstrated competitive performance in specific scenarios. This research highlights the importance of task-specific optimization, suggesting that hybrid approaches could improve game content generation by combining the strengths of different algorithms.

Suggested Citation

  • Sana Alyaseri & Andy Connor & Roopak Sinha, 2025. "Exploring Metaheuristic Algorithms for Enhanced Game Map Generation in Procedural Content Generation," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global Scientific Publishing, vol. 16(1), pages 1-33, January.
    • Handle: RePEc:igg:jamc00:v:16:y:2025:i:1:p:1-33
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