The ultimate goal of multiobjective optimization is to help a decision maker (DM) identify solution(s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting criteria. This can be realized by leveraging DM's preference information in evolutionary multiobjective optimization (EMO). No consensus has been reached on the effectiveness brought by incorporating preference in EMO (either a priori or interactively) versus a posteriori decision making after a complete run of an EMO algorithm. Bearing this consideration in mind, this article: 1) provides a pragmatic overview of the existing developments of preference-based EMO (PBEMO) and 2) conducts a series of experiments to investigate the effectiveness brought by preference incorporation in EMO for approximating various SOI. In particular, the DM's preference information is elicited as a reference point, which represents her/his aspirations for different objectives. The experimental results demonstrate that preference incorporation in EMO does not always lead to a desirable approximation of SOI if the DM's preference information is not well utilized, nor does the DM elicit invalid preference information, which is not uncommon when encountering a black-box system. To a certain extent, this issue can be remedied through an interactive preference elicitation. Last but not the least, we find that a PBEMO algorithm is able to be generalized to approximate the whole PF given an appropriate setup of preference information. © 1997-2012 IEEE.
Bibliographical noteThe work of Ke Li was supported in part by the UKRI Future Leaders Fellowship under Grant MR/S017062/1, and in part by the Royal Society under Grant IEC/NSFC/170243. The work of Xin Yao was supported in part by the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams under Grant 2017ZT07X386, in part by the Shenzhen Science and Technology Program under Grant KQTD2016112514355531, and in part by the Program for University Key Laboratory of Guangdong Province under Grant 2017KSYS008.
- evolutionary multiobjective optimization (EMO)
- preference incorporation
- reference point