Interactive multiobjective optimization methods incorporate preferences from a human
decision maker in the optimization process iteratively. This allows the decision maker to …

Some quality indicators have been proposed for benchmarking preference-based
evolutionary multi-objective optimization algorithms using a reference point. Although a …

Given the global economic and societal importance of the polymer industry, the continuous
search for improvements in the various processing techniques is of practical primordial …

The inherent knowledge limitations possessed by human experts often yield suboptimal
accuracy in linguistic decisions within traditional Decision Making Experimentation and …

In interactive multiobjective optimization methods, the preferences of a decision maker are
incorporated in a solution process to find solutions of interest for problems with multiple …

The robust decision-making framework (RDM) has been extended to consider multiple
objective functions and scenarios. However, the practical applications of these extensions …

Interactive multiobjective optimization methods operate iteratively so that a decision maker
directs the solution process by providing preference information, and only solutions of …

In multiobjective optimization problems, Pareto optimal solutions representing different
tradeoffs cannot be ordered without incorporating preference information of a decision …

Particle accelerators are among some of the largest science experiments in the world and
can consist of thousands of components with a wide variety of input ranges. These systems …

Solving multiobjective optimization problems with interactive methods enables a decision
maker with domain expertise to direct the search for the most preferred trade-offs with …