A Dynamic Multiobjective Optimization Algorithm with a New Prediction Model

被引：0
|
作者：
Li Z. [1 ]
Li Y. [1 ]
He L. [1 ]
Shen C. [2 ]
机构：
[1] National Key Laboratory of Science and Technology on Blind Signal Processing, Chengdu
[2] MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an
来源：
Shen, Chao | 2018年 / Xi'an Jiaotong University卷 / 52期
关键词：
Dynamic multiobjective optimization; Evolutionary algorithm; Kalman filter; Prediction model;
D O I：
10.7652/xjtuxb201810002
中图分类号：
学科分类号：
摘要：
A new dynamic multiobjective optimization algorithm is proposed to solve the problem that the existing dynamic multiobjective optimization algorithms have poor ability to track the rapid changing optimal solutions for practical applications, and the algorithm uses a new prediction model that combines the prediction value of central point and the vertical disturbance component. First, the central point of the optimal solution set before change is calculated as a prediction object, which changes the way that all solutions are usually used for prediction and improves the efficiency of the algorithm. Second, the history information of the algorithmic iterations is combined to select the location, velocity and acceleration as a state vector of prediction. The tracking ability for change in the solution set is ensured in most circumstances. At last, a hyperplane random disturbance that is perpendicular to the predicted direction of change is added to the predicted new solution to enhance the diversity of the knowledge set, and the convergence speed of the algorithm is improved. Experimental results show that the proposed algorithm is superior to the other 3 state-of-the-art dynamic multiobjective evolutionary algorithms in 75% test cases, and the run time of the algorithm is 39% lower than those of dynamic multiobjective optimization algorithms based on Kalman filter. © 2018, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
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页码：8 / 15
页数：7
相关论文
共 24 条
  • [11] Jiao R., Zeng S., Li X., Et al., Constrained optimization by solving equivalent dynamic constrained multi-objective based on learning, Journal of Wuhan University(Natural Science Edition), 63, 2, pp. 177-183, (2017)
  • [12] Chen M., Guo Y., Gong D., Et al., A novel dynamic multi-objective robust evolutionary optimization method, Acta Automatica Sinica, 11, pp. 2014-2032, (2017)
  • [13] Zhou A., Jin Y., Zhang Q., Et al., Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization, International Conference on Evolutionary Multi-Criterion Optimization, pp. 832-846, (2007)
  • [14] Costa E., Improving prediction in evolutionary algorithms for dynamic environments, Conference on Genetic and Evolutionary Computation, pp. 875-882, (2009)
  • [15] Muruganantham A., Zhao Y., Gee S.B., Et al., Dynamic multiobjective optimization using evolutionary algorithm with Kalman filter, Procedia Computer Science, 24, 1, pp. 66-75, (2013)
  • [16] Yan J., Yuan D., Xing X., Et al., Kalman filtering parameter optimization techniques based on genetic algorithm, IEEE International Conference on Automation and Logistics, pp. 1717-1720, (2008)
  • [17] Deb K., Kalyanmoy D., Multi-Objective Optimization Using Evolutionary Algorithms, pp. 1-20, (2001)
  • [18] Anupam T., Dipti S., Krishnendu S., Et al., A survey of multiobjective evolutionary algorithms based on decomposition, IEEE Transactions on Evolutionary Computation, 21, 3, pp. 440-462, (2017)
  • [19] Welch G., Bishop G., An Introduction to the Kalman Filter, pp. 1-16, (2001)
  • [20] 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, Lecture Notes in Computer Science, 4403, pp. 803-817, (2007)
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