IMPLEMENTATION OF A GENERIC FRAMEWORK ON CROWD SIMULATION: A NEW ENVIRONMENT TO MODEL CROWD BEHAVIOR AND DESIGN VIDEO GAMES

Öz

Crowd behavior is the collective act and gathering of a group of individuals to achieve a shared purpose. Swarm intelligence-based optimization algorithms are usually used to solve complex problems for crowd behavior. Crowd simulations are often used for the analyses that require precision in different domains such as complex structural analysis, image recognition, creating nature-inspired non-player character movements in video games, and more. In this study, a generic crowd simulation framework that can be used to simulate already-available crowd simulation algorithms and design new ones was developed. The test environment layout was generated with the use of a generate-and-test algorithm combined with the crowd simulation algorithms to make sure that the generated content is meeting the requirements of a crowd simulation environment. Within the framework, three different crowd simulation algorithms —firefly algorithm, particle swarm optimization, and artificial bee colony— are generated and also implemented as puzzle-like video games. The results show that all fireflies achieved to gather at the global minimum of the generated layout faster and in a more precise way than the artificial bee colony algorithm and particle swarm optimization algorithm. The developed framework enables a generic and parametric testbed to design and compare different algorithms and to generate video games.

Anahtar Kelimeler

• Crowd Simulation
• Firefly Algorithm
• Artificial Bee Colony Algorithm
• Particle Swarm Optimization
• Generate-and-Test Algorithm
• Game Generation

Kaynakça

1. Lin, Y., Chen, Y., “Crowd control with swarm intelligence”, 2007 IEEE Congress on Evolutionary Computation, 2007, 3321-3328.
2. Junaedi, H., Hariadi, M. and Purnama, I. K. E., “Multi agent with multi behavior based on particle swarm optimization (PSO) for crowd movement in fire evacuation”, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), 2013, 366-372.
3. Mckenzie, F. D. et al., “Integrating crowd-behavior modelling into military simulation using game technology”, Simulation & Gaming, 39 (1), 10-38, 2008.
4. Yang, X. S., “Firefly algorithm, stochastic test functions and design optimization”, Journal of Bio Inspired Computation, 2 (2), 78-84, 2010.
5. Dey, N. (Ed.), “Applications of Firefly Algorithm and its Variants: Case Studies and New Developments”, Springer Nature, 2020.
6. Yu, T. et al., “Modelling and Simulation of Evacuation Based on Bat Algorithm”, IOP Conference Series: Earth and Environmental Science, 267 (3), 032017, 2019.
7. Wang, G. G. et al., “Monarch butterfly optimization”, Neural computing and applications, 31 (7), 1995-2014, 2019.
8. Darwish, A., “Bio-inspired computing: Algorithms review, deep analysis, and the scope of applications”, Future Computing and Informatics Journal, 3(2), 231-246, 2018.

9. Kowalski, P. A., et al., “On the use of nature inspired metaheuristic in computer game", 2017 Federated Conference on Computer Science and Information Systems, 2017, 29-37.
10. Díaz, G., Ilgesias, A., “Evolutionary Behavioral Design of Non-Player Characters in a FPS Video Game Through Particle Swarm Optimization”, 13th International Conference on Software, Knowledge, Information Management and Applications, 2019, 1-8.
11. Ponticorvo, M. et al., “Approaches to Embed Bio-inspired Computational Algorithms in Educational and Serious Games”, CAID@ IJCAI, 2017.
12. Agarwal, S., Singh, A. P., Anand, N. “Evaluation performance study of Firefly algorithm, particle swarm optimization and artificial bee colony algorithm for non-linear mathematical optimization functions”, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013, 1-8.
13. Ackley, D., “A connectionist machine for genetic hillclimbing”, Springer Science & Bussiness Media, 28, 2012.
14. Togelius, J. et al., “Search-based procedural content generation: A taxonomy and survey”, IEEE Transactions on Computation Intelligence and AI in Games, 3 (3), 172-186, 2011.
15. Unity Technologies., Unity 3d., http://unity3d.com/, Retrieved on July 21, 2020.
16. Dented Pixel, LeanTween, https://assetstore.unity.com/packages/tools/animation/leantween-3595, Retrieved on July 21, 2020.
17. CraftPix, Assets, https://craftpix.net, Retrieved on July 21, 2020.
18. Karaboga, D., Ozturk, C., “A novel clustering approach: Artificial Bee Colony (ABC) algorithm”, Applied Soft Computing., 11 (1), 652-657, 2011.