DGO: Dice Game Optimizer

Year 2019, , 871 - 882, 01.09.2019

Mohammad Dehghanı , Zeinab Montazerı Om Parkash Malık

https://doi.org/10.35378/gujs.484643

Cited By: 31

Abstract

In recent years, optimization algorithms have been used in many applications. Most of these algorithms are inspired by physical processes or living beings' behaviors. This article suggests a new optimization method called “Dice Gaming Optimizer“ (DGO), which simulates dice gaming laws. This algorithm is inspired by an old game and the searchers are a set of players. Each player moves in the playground based on at least one and maximum six different players called guide’s players. The number of guide’s players for each player is determined by the number of dice. DGO is tested on 23 standard benchmark test functions and also compared with eight other algorithms such as: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS), Ant-Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm and Emperor Penguin Optimizer (EPO). Moreover, a real-life engineering design problem is solved by DGO. The results indicate that DGO have better performance as compared to the other well-known optimization algorithms.

Keywords

optimization, metaheuristic algorithms, dice, dice game, Dice Game Optimizer, game

References

• [1] K.-S. Tang, K.-F. Man, S. Kwong, and Q. He, "Genetic algorithms and their applications," IEEE signal processing magazine, vol. 13, pp. 22-37, 1996.
• [2] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, "Optimization by simulated annealing," science, vol. 220, pp. 671-680, 1983.
• [3] J. D. Farmer, N. H. Packard, and A. S. Perelson, "The immune system, adaptation, and machine learning," Physica D: Nonlinear Phenomena, vol. 22, pp. 187-204, 1986.
• [4] M. Drigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperation agents," IEEE Transactions of Systems, Man, and Cybernetics, pp. 29-41, 1996.
• [5] J. Kennedy, "Rc eberhart, ІParticle swarm optimization," in Proc. IEEE Conf. Neural Networks IV, Piscataway, NJ, 1995.
• [6] M. Dehghani, Z. Montazeri, A. Dehghani, and A. Seifi, "Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law," in Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on, 2017, pp. 0210-0214.
• [7] M. Bielli and P. Carotenuto, "Genetic Algorithms and Transportation Analysis: Review and Perspectives for Bus Network Optimization," in New Analytical Advances in Transportation and Spatial Dynamics, ed: Routledge, 2018, pp. 35-48.
• [8] T. H. Segall-Shapiro, E. D. Sontag, and C. A. Voigt, "Engineered promoters enable constant gene expression at any copy number in bacteria," Nature biotechnology, vol. 36, p. 352, 2018.
• [9] M. Dehghani, Z. Montazeri, A. Ehsanifar, A. R. Seifi, M. J. Ebadi, and O. M. Grechko, "PLANNING OF ENERGY CARRIERS BASED ON FINAL ENERGY CONSUMPTION USING DYNAMIC PROGRAMMING AND PARTICLE SWARM OPTIMIZATION," 2018, p. 10, 2018-10-19 2018.
• [10] Z. Montazeri and T. Niknam, "Energy carriers management based on energy consumption," in Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on, 2017, pp. 0539-0543.
• [11] P. Tarasewich and P. R. McMullen, "Swarm intelligence: power in numbers," Communications of the ACM, vol. 45, pp. 62-67, 2002.
• [12] T. Kohonen, Self-organization and associative memory vol. 8: Springer Science & Business Media, 2012.
• [13] Z. W. Geem, J. H. Kim, and G. Loganathan, "A new heuristic optimization algorithm: harmony search," Simulation, vol. 76, pp. 60-68, 2001.
• [14] M. Dorigo, V. Maniezzo, and A. Colorni, "Ant system: optimization by a colony of cooperating agents," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 26, pp. 29-41, 1996.
• [15] V. Gazi and K. M. Passino, "Stability analysis of social foraging swarms," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, pp. 539-557, 2004.
• [16] X. Yao, Y. Liu, and G. Lin, "Evolutionary programming made faster," Evolutionary Computation, IEEE Transactions on, vol. 3, pp. 82-102, 1999.
• [17] S. Mirjalili, "Genetic Algorithm," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 43-55.
• [18] S. Mirjalili, "Particle Swarm Optimisation," in Evolutionary Algorithms and Neural Networks, ed: Springer, 2019, pp. 15-31.
• [19] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, "GSA: a gravitational search algorithm," Information sciences, vol. 179, pp. 2232-2248, 2009.
• [20] P. Sarzaeim, O. Bozorg-Haddad, and X. Chu, "Teaching-Learning-Based Optimization (TLBO) Algorithm," in Advanced Optimization by Nature-Inspired Algorithms, ed: Springer, 2018, pp. 51-58.
• [21] H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, "Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems," Computers & Structures, vol. 110, pp. 151-166, 2012.
• [22] S. Mirjalili, S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in engineering software, vol. 69, pp. 46-61, 2014.
• [23] S. Saremi, S. Mirjalili, and A. Lewis, "Grasshopper optimisation algorithm: theory and application," Advances in Engineering Software, vol. 105, pp. 30-47, 2017.
• [24] G. Dhiman and V. Kumar, "Emperor Penguin Optimizer: A Bio-inspired Algorithm for Engineering Problems," Knowledge-Based Systems, 2018.
• [25] B. Kannan and S. N. Kramer, "An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design," Journal of mechanical design, vol. 116, pp. 405-411, 1994.