Hendrawan Armanto, Harits Ar Rosyid, Muladi Muladi, Gunawan Gunawan
Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an old evolutionary algorithm, it is still considered effective in achieving optimal solutions. The study found that PSO significantly improved the AI Team's Desire and enabled it to win against Default AI of similar levels or players with low MMR. However, it was still unable to defeat opponents with higher AI levels. Furthermore, this study is expected to assist other researchers in developing artificial intelligence in Dota 2, as the complexity of the development process lies not only in AI but also in language, structure, and communication between files. © 2024, the author(s).
Program Studi Teknik Elektro dan Informatika, Universitas Negeri Malang, Malang, Indonesia; Program Studi Informatika, Institut Sains dan Teknologi Terpadu Surabaya, Surabaya, Indonesia
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