In practical multicriterion decision making, it is cumbersome if a decision maker (DM) is asked to choose among a set of tradeoff alternatives covering the whole Pareto-optimal front. This is a paradox in conventional evolutionary multiobjective optimization (EMO) that always aim to achieve a well balance between convergence and diversity. In essence, the ultimate goal of multiobjective optimization is to help a DM identify solution(s) of interest (SOI) achieving satisfactory tradeoffs among multiple conflicting criteria. Bearing this in mind, this article develops a framework for designing preference-based EMO algorithms to find SOI in an interactive manner. Its core idea is to involve human in the loop of EMO. After every several iterations, the DM is invited to elicit her feedback with regard to a couple of incumbent candidates. By collecting such information, her preference is progressively learned by a learning-to-rank neural network and then applied to guide the baseline EMO algorithm. Note that this framework is so general that any existing EMO algorithm can be applied in a plug-in manner. Experiments on 48 benchmark test problems with up to ten objectives and a real-world multiobjective robot control problem fully demonstrate the effectiveness of our proposed algorithms for finding SOI. © 2023 IEEE.
Bibliographical noteThis work was supported in part by the UKRI Future Leaders Fellowship under Grant MR/S017062/1 and Grant MR/X011135/1; in part by NSFC under Grant 62076056; in part by the Royal Society under Grant IES/R2/212077; in part by EPSRC under Grant 2404317; and in part by the Amazon Research Award.
- Evolutionary multiobjective optimization (EMO)
- gradient descent
- learning to rank (LTR)
- preference modeling