Clustering Analysis for the Pareto Optimal Front in Multi-Objective Optimization

Bio-inspired algorithms are a suitable alternative for solving multi-objective optimization problems. Among different proposals, widely used approach is based on the Pareto front. In this document, proposal made analysis of optimal front problems using clustering techniques. With approach, an sought further use and improvement considering solutions clusters found. To carry out clustering, methods k-means fuzzy c-means employed, in such way that there two alternatives to generate possible clusters. Regarding results, it observed both perform adequate separation continuous fronts; discontinuous fronts, obtain results complement each other (there no superior algorithm). terms processing time, presents less execution time than c-means.