Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rank

I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool

With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for mulitple conflicting objectives. During the past decade of research and application, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of proc...

Hierarchical Approach to Evolutionary Multi-Objective Optimization

In this paper a new “hierarchical” evolutionary approach to solving multi-objective optimization problems is introduced. The results of experiments with standard multi-objective test problems, which were aimed at comparing “hierarchical” and “classical” versions of multiobjective evolutionary algorithms, show that the proposed approach is a very promising technique.

Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functions

Decomposition has become an increasingly popular technique for evolutionary multiobjective optimization (EMO). A decomposition-based EMO algorithm is usually designed to approximate a whole Pareto-optimal front (PF). However, in practice, the decision maker (DM) might only be interested in her/his region of interest (ROI), i.e., a part of the PF. Solutions outside that might be useless or even ...

Evolutionary Multi-Objective Robust Optimization

Optimum Design of Pultrusion Process via Evolutionary Multi-objective Optimization

Pultrusion is one of the most cost-effective manufacturing techniques for producing fiber reinforced composites with constant cross sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters, i.e. pulling speed, power and dimensions of the heating platens, length and width of the heating die, design of the res...