Multiobjective Local Search as an Initialization Procedure for Evolutionary Approaches to Polygonal Approximation

Abstract

Polygonal approximation is a process based on the division of a closed curve into a shorter set of segments. This problem has been traditionally approached as a single-objective optimization issue where the representation error was minimized according to a set of restrictions and parameters, or the number of segments minimized keeping the error value bellow a certain value. When these approaches try to be subsumed into more recent multi-objective ones, which attempt to provide a algorithm to handle these two objectives jointly, a number of issues arise. Current work successfully adapts two of these traditional approaches, bottom-up and top-down algorithms, and introduces them as initialization procedures for a multiobjective evolutionary algorithm for polygonal approximation, being the results, both for initial and final fronts, analyzed according to their statistical significance over a set of traditional curves from the domain.