Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.procs.2013.10.028
Title: Dynamic multiobjective optimization using evolutionary algorithm with kalman filter
Authors: Muruganantham, A.
Zhao, Y.
Gee, S.B.
Qiu, X.
Tan, K.C. 
Keywords: dynamic multiobjective optimization
evolutionary algorithm
Evolutionary Multiobjective (EMO)
Kalman Filter
scoring scheme
Issue Date: 2013
Source: Muruganantham, A., Zhao, Y., Gee, S.B., Qiu, X., Tan, K.C. (2013). Dynamic multiobjective optimization using evolutionary algorithm with kalman filter. Procedia Computer Science 24 : 66-75. ScholarBank@NUS Repository. https://doi.org/10.1016/j.procs.2013.10.028
Abstract: Multiobjective optimization is a challenging task, especially in a changing environment. The study on dynamic multiobjective optimization is so far very limited. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. In this paper, a Kalman Filter prediction-based evolutionary algorithm is proposed to solve dynamic multiobjective optimization problems. This prediction model uses historical information to predict for future generations and thus, direct the search towards the Pareto optimal solutions. A scoring scheme is then devised to further enhance the performance by hybridizing the Kalman Filter prediction model with the random re-initialization method. The proposed models are tested and analysis of the experiment results are presented. It is shown that the proposed models are capable of improving the performances, as compared to using random re-initialization method alone. The study also suggests that additional features could be added to the proposed models for improvements and much more research in this field is still needed. © 2013 The Authors.
Source Title: Procedia Computer Science
URI: http://scholarbank.nus.edu.sg/handle/10635/70044
ISSN: 18770509
DOI: 10.1016/j.procs.2013.10.028
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