TY - GEN
T1 - Solving dynamic multi-objective optimization problems via support vector machine
AU - Jiang, Min
AU - Hu, Weizhen
AU - Qiu, Liming
AU - Shi, Minghui
AU - Tan, Kay Chen
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (No.61673328).
Publisher Copyright:
© 2018 IEEE.
PY - 2018/6/8
Y1 - 2018/6/8
N2 - Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.
AB - Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.
KW - Dynamic Multi-objective Optimization Problems
KW - Pareto Optimal Set
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85049772256&partnerID=8YFLogxK
U2 - 10.1109/ICACI.2018.8377567
DO - 10.1109/ICACI.2018.8377567
M3 - Conference article published in proceeding or book
AN - SCOPUS:85049772256
T3 - Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
SP - 819
EP - 824
BT - Proceedings - 2018 10th International Conference on Advanced Computational Intelligence, ICACI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Advanced Computational Intelligence, ICACI 2018
Y2 - 29 March 2018 through 31 March 2018
ER -