Abstract
We choose the well-known evolution strategy (ES) in the evolutionary computation (EC) community to solve the large-scale scheduling problem provided by Alibaba cloud services. Since the problem is accompanied by multiple strong constraints, we design two additional strategies for improving the search efficiency with a given limited computational cost. Compared with widely used numerical benchmark test suits, this problem arises from the requirements of real-world applications and has strict constraints that cannot be violated, such as processing time, response timeout, load balance, and so on. The main contribution of this paper is to establish a bridge between EC algorithms and the characteristics of real-world problems so that EC algorithms can solve real-world problems more effectively and smoothly. Based on the difficulties encountered in the experiment, we summarize some of our experiences and insights, and hope that they may bring new enlightenment to the latecomers.
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References
Votaw, D.F.: Methods of solving some personnel-classification problems. Psychometrika 17(3), 255–266 (1952). https://doi.org/10.1007/BF02288757
Wolfe, P.: Recent developments in nonlinear programming. Adv. Comput. 3, 155–187 (1962)
Back, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: comments on the history and current state. IEEE Trans. Evol. Comput. 1(1), 3–17 (1997)
Xiao, Q.G., Li, C.B., Tang, Y., Pan, J., Yu, J., Chen, X.Z.: Multi-component energy modeling and optimization for sustainable dry gear hobbing. Energy 187, 1–16 (2019)
Holland, J.H.: Outline for a logical theory of adaptive systems. J. ACM 9(3), 297–314 (1962)
Kicinger, R., Arciszewski, T., Jong, K.D.: Evolutionary computation and structural design: a survey of the state-of-the-art. Comput. Struct. 83(23–24), 1943–1978 (2005)
Beyer, H.G., Schwefel, H.P.: Evolution strategies: a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002). https://doi.org/10.1023/A:1015059928466
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Storn, R., Price, K.: Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997). https://doi.org/10.1023/A:1008202821328
Dervis, K., Bahriye, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x
Gandomi, A.H., Alavi, A.H.: Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)
Yu, J., Takagi, H.: Acceleration for fireworks algorithm based on amplitude reduction strategy and local optima-based selection strategy. In: Tan, Y., Takagi, H., Shi, Y. (eds.) ICSI 2017. LNCS, vol. 10385, pp. 477–484. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61824-1_52
Yu, J., Takagi, H., Tan, Y.: Accelerating the fireworks algorithm with an estimated convergence point. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10941, pp. 263–272. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93815-8_26
Jin, Y., Markus, O., Bernhard, S.: A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 6(5), 484–494 (2002)
Yu, J., Pei, Y., Takagi, H.: Accelerating evolutionary computation using estimated convergence points. In: IEEE Congress on Evolutionary Computation, pp. 1438–1444 (2016)
Pei, Y., Yu, J., Takagi, H.: Search acceleration of evolutionary multi-objective optimization using an estimated convergence point. Mathematics 7(2), 129–147 (2019)
Yu, J., Takagi, H., Tan, Y.: Fireworks algorithm for multimodal optimization using a distance-based exclusive strategy. In: IEEE Congress on Evolutionary Computation, pp. 2215–2220 (2019)
Niu, B., Wang, H., Wang, J., Tan, L.: Multi-objective bacterial foraging optimization. Neurocomputing 116, 336–345 (2013)
Niu, B., Wang, J., Wang, H.: Bacterial-inspired algorithms for solving constrained optimization problems. Neurocomputing 148, 54–62 (2015)
The homepage of the competition problem (in Chinese) (2020). https://tianchi.aliyun.com/competition/entrance/531831/information
Acknowledgments
This work was supported in part by Natural Science Foundation of China under Grant 62001302, in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2019A1515110401, 2019A1515111205, 2021A1515011348, and in part by Natural Science Foundation of Shenzhen under Grant JCYJ20190808145011259, and in part by Shenzhen Science and Technology Program under Grant RCBS20200714114920379.
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Yu, J., Li, Y., Zhou, T., Zhang, C., Yue, G., Ge, Y. (2021). Performance Analysis of Evolutionary Computation Based on Tianchi Service Scheduling Problem. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12689. Springer, Cham. https://doi.org/10.1007/978-3-030-78743-1_14
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