A timing efficient method for interactive multi-objective optimization algorithms
DOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE9.2025.72-82Keywords:
Interactive evolutionary multi-objective optimization; DMEA-II; NSGA-II; MOEA/D.Abstract
Multi-objective optimization has been increasingly applied in many real-world domains. Evolutionary algorithms are widely adopted because they can approximate diverse sets of trade-off solutions in a single run. However, the output of these algorithms is a set of Pareto-optimal solutions, and selecting the most appropriate solution depends heavily on the decision maker (DM). In interactive multi-objective evolutionary optimization, the DM’s feedback can also influence the search trajectory, guiding the population toward preferred regions without imposing hard constraints. Nevertheless, incorporating user preferences during evolution must be performed carefully. Multi-objective evolutionary algorithms rely on a delicate balance between convergence and diversity, as well as between exploration and exploitation. Poorly timed interactions may disrupt this balance, causing loss of diversity or premature convergence. This paper proposes a timing-suggestion mechanism that identifies when user interaction is most effective. The method analyzes population dynamics and quality indicators to determine appropriate moments for incorporating DM feedback, ensuring that interaction enhances, rather than destabilizes, the search process. Experimental results on interactive multi-objective optimization algorithms demonstrate that the proposed approach improves interaction effectiveness while maintaining competitive optimization performance.
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