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

This study investigated the performance of various metaheuristic algorithms in Procedural Content Generation, specifically focusing on generating game map layouts. It challenges the common use of Genetic Algorithms by exploring alternative optimization approaches for PCG tasks. Three metaheuristic algorithms: Genetic algorithms, Particle Swarm Optimization, and Artificial Bee Colony were compared for their effectiveness and efficiency in generating game levels. Comprehensive experiments were conducted by using each algorithm for map creation. Metrics such as the convergence speed and content quality were used to evaluate the generated maps and identify the strengths and weaknesses of each algorithm in this context. The findings reveal that GA using tournament selection outperform other GAs implementations such as ABC and PSO for generating game maps. This leads to the conclusion that the choice of the optimal metaheuristic algorithm for PCG depends on the specific task and the selected evaluation methods. In addition, this study suggests that integrating diverse metaheuristic approaches can enrich the field of PCG and inspire game developers to utilize a broader range of techniques. By incorporating various algorithms, developers can potentially enhance content generation and create more immersive and engaging game experiences.