References
- A. Górski and M. Ogorzałek, “Concurrent Real-Time Optimization of Detecting Unexpected Tasks in IoT Design Process Using GA,” Late Breaking Papers from the IEEE 2023 Congress on Evolutionary Computation Chicago, IL, USA, pp. 74–77, 2024. doi: 10.5281/zenodo.10046 580
- A. Górski and M. Ogorzałek, “Concurrent Real-Time Optimization in Embedded System Design Process Using Genetic Algorithm”, 5th Polish Conference on Artificial Intelligence, Warsaw, Poland, Apr. 2024.
- X. Liang and Y. Xiao, “Game Theory for Network Security,” IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 472–486, 2013.
- D. Sanchez and H. Florez. “Improving Game Modeling for the Quoridor Game State Using GraphDatabases,” in Proceedings of the International Conference on Information Technology & Systems (ICITS 2018), Á. Rocha and T. Guarda, Eds., Advances in Intelligent Systems and Computing, vol 721. Cham: Springer, 2018. doi: 10.1007/97 8-3-319-73450-7_32.
- J. Konert, V. Wendel, S. Göbel, and R. Steinmetz, “Towards an Analysis of Cooperative Learning-Behaviour in Social Dilemma Games,” in Proceedings of the European Conference on Games-based Learning, 2011, pp. 329–332.
- I. Grabska-Gradzińska, L. Nowak, and E. Grabska, “Towards an Analysis of Cooperative Learning-Behaviour in Social Dilemma Games,” in Artificial Intelligence and Soft Computing (ICAISC 2020), Lecture Notes in Computer Science, vol. 12415. Cham: Springer, 2020. doi: 10.1007/978-3-030-61401-0_37.
- F. Laamarti, M. Eid, and A. El Saddik, “An Overview of Serious Games,” International Journal of Computer Games Technology, pp. 1–15, 2014. doi: 10.1155/2014/358152.
- A. Min, H. Min, and S. Kim, “Effectiveness of Serious Games in Nurse Education: A Systematic Review,” Nurse Education Today, vol. 108, 105178, Elsevier, 2022.
- W. Johnson, “Serious Use of a Serious Game for Language Learning,” International Journal of Artificial Intelligence in Education, vol. 20, 2010, pp. 175–195.
- M. Mortara, C. E. Catalano, F. Bellotti, G. Fiucci, M. Houry-Panchetti, and P. Petridis, “Learning Cultural Heritage by Serious Games,” Journal of Cultural Heritage, 15(3), pp. 318–325, 2014.
- K. Madani, T. W. Pierce, and A. Mirchi, “Serious Games on Environmental Management,” Sustainable Cities and Society, vol. 29, 2017, pp. 1–11.
- S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Advances in Engineering Software. vol. 69, pp. 46–61, 2014. doi: 10.1016/j. advengsoft.2013.12.007.
- D. Wang, D. Tan, and L. Liu, “Particle Swarm Optimization Algorithm: An Overview,” Soft Computing, vol. 22, pp. 387–408, 2018. doi: 10.1007/s0 0500-016-2474-6.
- S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper Optimization Algorithm: Theory and Application,” Advances in Engineering Software, vol. 105, pp. 30–47, 2017.
- S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, May 2016.
- S. Mirjalili, “Dragonfly Algorithm: A New Meta-Heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-Objective Problems,” Neural Computing and Applications, vol. 27, no. 4, pp. 1053–1073, 2016.
- P. Hu, J. Pan, and S. Chu, “Improved Binary Grey Wolf Optimizer and Its Application for Feature Selection,” Knowledge-Based Systems, Vol. 195, 105746, 2020, doi: 10.1016/j.knosys.2020.10 5746
- Y. Wang, T. Wang, S. Dong, and C. Yao, “Improved Binary Grey Wolf Optimizer and Its Application for Feature Selection,” Journal of Physics: Conference Series, vol. 1682, Article 012020, 2020. doi: 10.1088/1742-6596/1682/1/012020
- A. Saxena, R. Kumar, and S. Das “-Chaotic map enabled Grey Wolf Optimizer,” Applied Soft Computing, vol. 75, Elsevier, pp. 84–105, February 2019.
- H. Yu, Y. Yu, Y. Liu, Y. Wang, and S. Gao, “Chaotic Grey Wolf Optimization,” in Proceedings of the 2016 International Conference on Progress in Informatics and Computing (PIC), Shanghai, China, 2016, pp. 103–113. doi: 10.1109/PIC.20 16.7949476.
- K. Meidani, A. Hemmasian, S. Mirjalili, and A. B. Farimani, “Adaptive Grey Wolf Optimizer,” Neural Computing and Applications, vol. 34, pp. 7711–7731, 2022. doi: 10.1007/s00521-021-06885-9
- W. Gu, “An Improved Multi-Objective Grey Wolf Optimization Algorithm with Dynamic Chaos Local Search Mechanism,” in Proceedings of the 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 2020, pp. 2020–2024. doi: 10.1109/ITAIC49862.2020.9338760
- S. Bahuguna and A. Pal, “-Hill Climbing Grey Wolf Optimizer,” in Soft Computing for Problem Solving, A. Tiwari, K. Ahuja, A. Yadav, J. C. Bansal, K. Deep, and A. K. Nagar, Eds., Advances in Intelligent Systems and Computing, vol. 1393. Singapore: Springer, 2021. doi: 10.1007/978-981-16-2712-5_26
- Q. Fan, H. Huang, Y. Li, Z. Han, Y. Hu, and D. Huang, “An Improved Multi-Objective Grey Wolf Optimization Algorithm with Dynamic Chaos Local Search Mechanism,” Expert Systems with Applications, vol. 165, 2020.
- Y. Yang, B. Yang, S. Wang, W. Liu, and T. Jin, “An Improved Grey Wolf Optimizer Algorithm for Energy-Aware Service Composition in Cloud Manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 105, pp. 3079–3091, 2019. doi: 10.1007/s00170-019-04449-9
- J. Jiang, Z. Zhao, Y. Liu, W. Li, and H. Wang, “DSGWO: An Improved Grey Wolf Optimizer with Diversity Enhanced Strategy Based on Group-Stage Competition and Balance Mechanisms,” Knowledge-Based Systems, vol. 250, art. no. 109100, Aug. 2022.
- M. H. Nadimi-Shahraki, S. Taghian, and S. Mirjalili, “An Improved Grey Wolf Optimizer for Solving Engineering Problems,” Expert Systems with Applications, vol. 166, 113917, 2021. doi: 10.101 6/j.eswa.2020.113917
- J. Pan, Y. Gao, Q. Qian, Y. Feng, Y. Fu, M. Sun, and F. Sardari, “An Improved Grey Wolf Optimizer for Solving Engineering Problems,” Optik, vol. 242, art. no. 167150, 2021, doi: 10.1016/j.ijleo.20 21.167150
- F. A. Saif, R. Latip, Z. M. Hanapi, and K. Shafinah, “Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing,” IEEE Access, vol. 11, pp. 20635–20646, 2023, doi: 10.1109/ACCESS.2023.3241240
- S. Mangalampalli, G. R. Karri, and M. Kumar, “Multi-Objective Task Scheduling Algorithm in Cloud Computing Using Grey Wolf Optimization,” Cluster Comput., vol. 26, pp. 3803–3822, 2023. doi: 10.1007/s10586-022-03786-x
- C. Qu, W. Gai, M.Zhong, and J. Zhang, “A Novel Reinforcement Learning-Based Grey Wolf Optimizer Algorithm for Unmanned Aerial Vehicles (UAVs) Path Planning,” Applied Soft Computing, vol. 89, 106099, 2020, doi: 10.1016/j.asoc.2020. 106099
- J. Liu, X. Wei, and H. Huang, “An Improved Grey Wolf Optimization Algorithm and Its Application in Path Planning,” IEEE Access, vol. 9, pp. 121944–121956, 2021, doi: 10.1109/acce ss.2021.3108973
- A. M. Górski and M. Ogorzałek, “An Improved Grey Wolf Optimization Algorithm and Its Application in Path Planning,” in Proceedings of the 21st International SoC Design Conference (ISOCC), Sapporo, Japan, pp. 398–399, August 2024, doi: 10.1109/ISOCC62682.2024.10762129
- R. P. Dick, D. L. Rhodes, and W. Wolf, “TGFF: Task Graphs for Free,” in Proc. Workshop on Hard-ware/Software Codesign, 1998, pp. 97–101.
- D. J. Abraham, K. Cechlárová, D. Manlove, and K. Mehlhorn, “Pareto Optimality in House Allocation Problems,” in Proc. 16th Int. Symp. Algorithms Comput. (ISAAC), Lecture Notes in Computer Science, vol. 3341, Springer, 2005, pp. 1163–1175.
- A. M. Górski, “Extended Task Graph for Real-Life Optimization Problems,” in Proceedings of the 7th International Conference on the Dynamics of Information Systems, Lecture Notes in Computer Science, vol. 14661, Springer, 2025, pp. 74–86.
