Support to Select Satisfying Solutions Using Visualization Method in Multi-Objective Optimization Problem

Recently, the rapid progress of computers introduces evolutionary computations to next step, which is the demand for the variety of Pareto solutions in multi-objective optimization problems. We can acquire a large amount of Pareto solutions in a short time. However, it is difficult to grasp the acquired Pareto solutions effectivelly, because the Pareto solutions have multi-dimensional fitness values. This paper tries to support a user to select satisfying solutions with visualization method. This paper applies a visualization method to acquired Pareto solutions which have multi-objective fitness values. It enables us to grasp the distributed structure of Pareto solutions and clarify the relationship among multi-objective fitness values. This paper shows that the visualization result enables us to interpret the characteristics of Pareto solutions through experimental result of Nurse Scheduling Problem (NSP) which is one of the multi-objective optimization problems.