In single-objective optimization problems, which have only one optimal design objective, the absolute optimal solution to maximize/minimize the objective function could be determined. However, in the most of real design problems, optimization problems become multi-objective, which two or more design objectives must be optimized simultaneously, and no single absolute optimal solution is existed. In these cases, recognizing what kind of alternative solutions exist in Pareto optimal sets seems to be useful for designers who have to decide an acceptable solution. In this paper, the authors carried out multi-objective optimization using multi-objective genetic algorithm through a case study involved in the real indoor environmental design - window design. Then the authors analyzed structure of Pareto optimal sets. Here we present the analysis process as well as the case study details, and show how the method proposed here is effective to decide an acceptable solution in multi-objective optimization problem.
- Design support
- Luminous environment
- Multi-objective genetic algorithm
- Pareto optimal solution
- Thermal environment