TY - JOUR
T1 - Information-Utilization-Method-Assisted Multimodal Multiobjective Optimization and Application to Credit Card Fraud Detection
AU - Han, Shoufei
AU - Zhu, Kun
AU - Zhou, Mengchu
AU - Cai, Xinye
N1 - Funding Information:
Manuscript received October 7, 2020; revised January 14, 2021; accepted February 16, 2021. Date of publication March 25, 2021; date of current version August 2, 2021. This work was supported by the National Natural Science Foundation of China under Grant 62071230 and Grant 62061146002. (Corresponding authors: Kun Zhu; MengChu Zhou.) Shoufei Han, Kun Zhu, and Xinye Cai are with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China, and also with the Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China (e-mail: hanshoufei@gmail.com; zhukun@nuaa.edu.cn; xinye@nuaa.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/8
Y1 - 2021/8
N2 - Different from multiobjective optimization problems (MOPs), multimodal MOPs (MMOPs) focus on both decision and objective spaces rather than only objective one. Thus, finding a good Pareto front approximation and finding the maximal number of equivalent Pareto optimal solutions for each objective vector in the Pareto front are two core tasks for them. Although some multimodal multiobjective evolutionary algorithms have been proposed to handle them, they can quickly converge to the easy-to-find equivalent Pareto optimal solutions, thereby losing their ability to improve solution diversity in decision space and performance in objective space. To address the above issues, this work proposes a new information utilization method. Its core idea is to randomly extract a certain amount of decision variable information from the current optimal solutions to construct an information vector, which is, in turn, used to assist the generation of elite solutions. The proposed method can assist any available intelligent optimizers to improve their performance in solving MMOPs. This is confirmed by experimental results obtained from solving 22 such problems from CEC2019 and 12 scalable imbalanced distance minimization problems through a number of optimizers. Finally, we apply the proposed method to credit card fraud detection problems to show its practical significance.
AB - Different from multiobjective optimization problems (MOPs), multimodal MOPs (MMOPs) focus on both decision and objective spaces rather than only objective one. Thus, finding a good Pareto front approximation and finding the maximal number of equivalent Pareto optimal solutions for each objective vector in the Pareto front are two core tasks for them. Although some multimodal multiobjective evolutionary algorithms have been proposed to handle them, they can quickly converge to the easy-to-find equivalent Pareto optimal solutions, thereby losing their ability to improve solution diversity in decision space and performance in objective space. To address the above issues, this work proposes a new information utilization method. Its core idea is to randomly extract a certain amount of decision variable information from the current optimal solutions to construct an information vector, which is, in turn, used to assist the generation of elite solutions. The proposed method can assist any available intelligent optimizers to improve their performance in solving MMOPs. This is confirmed by experimental results obtained from solving 22 such problems from CEC2019 and 12 scalable imbalanced distance minimization problems through a number of optimizers. Finally, we apply the proposed method to credit card fraud detection problems to show its practical significance.
KW - Credit card fraud detection
KW - Elite solutions
KW - Feature selection
KW - INformation Utilization Method (INUM)
KW - Information vector
KW - Multimodal multiobjective evolutionary algorithms (MMEAs)
KW - Multimodal multiobjective optimization problems (MMOPs)
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U2 - 10.1109/TCSS.2021.3061439
DO - 10.1109/TCSS.2021.3061439
M3 - Article
AN - SCOPUS:85103240661
SN - 2329-924X
VL - 8
SP - 856
EP - 869
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 4
M1 - 9387114
ER -