Abstract
There are a large number of multi-objective optimization problems in real-world applications, like in games, that need to be solved in real time. In order to meet this pressing need, we suggests a method of parallelizing the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Furthermore, a novel task decomposition strategy and scalarizing method without the ideal point are proposed for meeting the requirements of real-time and precision of the game. By combining the novel scalarizing function and GPU-based CUDA technology with the MOEA/D, a parallel MOEA/D for real-time multi-objective optimization problems is developed, namely P-MOEA/D. Experimental studies on ZDT and DTLZ benchmark problems suggest that the P-MOEA/D algorithm is efficient and fast.
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References
Zhen, J.S., Watson, I.: Neuroevolution for micromanagement in the real-time strategy game StarCraft: brood war. In: Cranefield, S., Nayak, A. (eds.) AI 2013. LNCS (LNAI), vol. 8272, pp. 259–270. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-03680-9_28
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)
Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Li, H., Zhang, Q.: Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Trans. Evol. Comput. 13(2), 284–302 (2009)
Liu, H., Gu, F., Zhang, Q.: Decomposition of a multi-objective optimization problem into a number of simple multiobjective subproblems. IEEE Trans. Evol. Comput. 18(3), 450–455 (2014)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)
Ma, X., Qi, Y., Li, L., et al.: MOEA/D with uniform decomposition measurement for many-objective problems. Soft Comput. - A Fusion Found. Methodol. Appl. 18(12), 2541–2564 (2014)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Da Fonseca Grunert, V.: Performance assessment of multi-objective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)
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Yu, J., Li, L., Qi, Y. (2019). Parallel MOEA/D for Real-Time Multi-objective Optimization Problems. In: El Rhalibi, A., Pan, Z., Jin, H., Ding, D., Navarro-Newball, A., Wang, Y. (eds) E-Learning and Games. Edutainment 2018. Lecture Notes in Computer Science(), vol 11462. Springer, Cham. https://doi.org/10.1007/978-3-030-23712-7_31
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DOI: https://doi.org/10.1007/978-3-030-23712-7_31
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