Comparing interactive evolutionary multiobjective optimization methods with an artificial decision maker
AbstractSolving multiobjective optimization problems with interactive methods enables a decision maker with domain expertise to direct the search for the most preferred trade-offs with preference information and learn about the problem. There are different interactive methods, and it is important to compare them and find the best-suited one for solving the problem in question. Comparisons with real decision makers are expensive, and artificial decision makers (ADMs) have been proposed to simulate humans in basic testing before involving real decision makers. Existing ADMs only consider one type of preference information. In this paper, we propose ADM-II, which is tailored to assess several …
Designing empirical experiments to compare interactive multiobjective optimization methods
Interactive multiobjective optimization methods operate iteratively so that a decision maker directs the solution process by providing preference information, and only solutions of interest are generated. These methods limit the amount of information considered in each iteration and support the decision maker in learning about the trade-offs. Many interactive methods have been developed, and they differ in technical aspects and the type of preference information used. Finding the most appropriate method for a problem to be solved is challenging, and supporting the selection is crucial. Published research lacks information on the conducted experiments’ specifics (e.g. questions asked), makin…