Comparative Analysis of Metaheuristic Algorithms for Procedural Race Track Generation in Games

Author:

Alyaseri Sana1ORCID,Conner Andy2ORCID

Affiliation:

1. Whitecliffe College, New Zealand

2. Auckland University of Technology, New Zealand

Abstract

Procedural Content Generation (PCG) aims to automatically generate the content of games using algorithmic approaches, as this can reduce the cost of game design and development. PCG algorithms can be applied to all elements of a game, including terrain, maps, stories, dialogues, quests, and characters. A wide variety of search algorithms can be applied to PCG problems; however, those most often used are variations of evolutionary algorithms. This study focuses on comparing three metaheuristic approaches applied to racetrack games, with the specific goal of evaluating the effectiveness of different algorithms in producing game content. To that end, a Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO) are applied to a game-level design task to attempt to identify any discernible differences in their performance and identify whether alternative algorithms offer desirable performance characteristics. The results of the study indicate that both the ABC and PSO approaches offer potential advantages to Genetic Algorithm implementation.

Publisher

IGI Global

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Context-Aware Approach Framework to Algorithm Selection for Search Based Procedural Content Generation;2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT);2024-07-04

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