Prediction strategy based on reference line for dynamic multi-objective optimization

被引：0
|
作者：
Li E.-C. [1 ]
Zhao Y.-M. [1 ]
机构：
[1] College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou
来源：
Li, Er-Chao (lecstarr@163.com) | 1600年 / Northeast University卷 / 35期
关键词：
Dynamic multi-objective optimization; Guide-individual; Prediction; Reference line;
D O I：
10.13195/j.kzyjc.2018.1442
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In order to track the Pareto front and Pareto solution set of dynamic multi-objective optimization problems quickly and accurately, an algorithm based on a reference line prediction strategy is proposed to solve dynamic multiobjective optimization problems without relying on historical information. The algorithm predicts the direction of the optimal individual by recording the changes of individual position at the beginning of the environmental change and a short period of time after the individual's self-evolution. At the same time, non-dominated individuals associated with each reference line are selected as the guided individuals according to the uniform distribution of several extend individuals in this direction. The results of 7 benchmark problems and the comparison with other two existing dynamicmulti-objective algorithms indicate that the proposed algorithm can maintain better performance in dealing with dynamic multi-objective problems. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：1547 / 1560
页数：13
相关论文
共 22 条
  • [1] Guo Yi-nan, Cheng Jian, Luo Sha, Et al., Robust dynamic multi-objective vehicle routing optimization method, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15, 6, pp. 1891-1903, (2018)
  • [2] Farina M, Deb M, Amato P., Dynamic multi-objective optimization problems: Test cases, approximations, and applications, IEEE Transactions on Evolutions Computation, 8, 5, pp. 425-442, (2004)
  • [3] Zheng J H., Multiple objective evolutionary algorithms and its applications, pp. 12-38, (2007)
  • [4] Coello C A C, Lamont G B, Veldhuizen D A V., Evolutionary algorithms for solving multi-objective problems, (2002)
  • [5] Deb K, Udaya B R N, Karthik S., Dynamic multi-objective optimization and decision--Making using modified NSGA-Ⅱ: A case study on hydro-thermal power scheduling, Evolutionary Multi-criterion Optimization, pp. 803-817, (2007)
  • [6] Zhang Z., Multi-objective optimization immune algorithm in dynamic environments and its application to greenhouse control, Applied Soft Computing, 8, 2, pp. 959-971, (2008)
  • [7] Liu R, Zhang W, Jiao L, Et al., A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization, Proceedings of Genetic and Evolutionary Computation Conference, pp. 423-430, (2010)
  • [8] Azevedo C R B, Araujo A F R., Generalized immigration schemes for dynamic evolutionary multi-objective optimization, IEEE Congress on Evolutionary Computation, pp. 2033-2040, (2011)
  • [9] Wei J, Wang Y., Hyper rectangle search based particle swarm algorithm for dynamic constrained multi-objective optimization problems, IEEE World Congress on Computational Intelligence, pp. 1-8, (2012)
  • [10] Hatzakis I, Wallace D., Dynamic multi-objective optimization with evolutionary algorithms: A forward- looking approach, Conference on Genetic and Evolutionary Computation, pp. 1201-1208, (2006)
123