TY - GEN
T1 - Acceptability of a decision maker to handle multi-objective optimization on design space
AU - Inoue, Makoto
AU - Matsumoto, Hibiki
AU - Takagi, Hideyuki
N1 - Funding Information:
This work was supported by JSPS KAKENHI Grant Number 18K11470 and 20K12524.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/5
Y1 - 2020/12/5
N2 - We introduce the acceptability of a decision maker to handle evolutionary multi-objective optimization (EMO) on design space, while most of EMO research tries to find many solutions on an objective space and passes them to a decision maker. Unlike this conventional EMO approaches, our approach decides maker's model with the concept of acceptability and introduces it in EMO search. Especially, this approach works well when qualitative factors, such as the decision maker's experience and knowledge on a task, are a part of evaluations. Acceptability functions for each of objectives are aggregated firstly, and the aggregated acceptability forms contours on an objective space and is mapped on a design space. The acceptability contours on a design space can narrow down the area of solutions. We could find better solutions in our experiments than the conventional approach of searching solutions on an objective space.
AB - We introduce the acceptability of a decision maker to handle evolutionary multi-objective optimization (EMO) on design space, while most of EMO research tries to find many solutions on an objective space and passes them to a decision maker. Unlike this conventional EMO approaches, our approach decides maker's model with the concept of acceptability and introduces it in EMO search. Especially, this approach works well when qualitative factors, such as the decision maker's experience and knowledge on a task, are a part of evaluations. Acceptability functions for each of objectives are aggregated firstly, and the aggregated acceptability forms contours on an objective space and is mapped on a design space. The acceptability contours on a design space can narrow down the area of solutions. We could find better solutions in our experiments than the conventional approach of searching solutions on an objective space.
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U2 - 10.1109/SCISISIS50064.2020.9322679
DO - 10.1109/SCISISIS50064.2020.9322679
M3 - Conference contribution
AN - SCOPUS:85100389871
T3 - 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
BT - 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
Y2 - 5 December 2020 through 8 December 2020
ER -