Mario Guillermo Leguizamón

Approximated algorithms for the minimum dilation triangulation problem

The complexity status of the minimum dilation triangulation (MDT) problem for a general point set is unknown. Therefore, we focus on the development of approximated algorithms to find high quality triangulations of minimum dilation. For an initial approach, we design a greedy strategy able to obtain approximate solutions to the optimal ones in a simple way. We also propose an operator to generate the neighborhood which is used in different algorithms: Local Search, Iterated Local Search, and Simulated Annealing. Besides, we present an algorithm called Random Local Search where good and bad solutions are accepted using the previous mentioned operator. For the experimental study we have created a set of problem instances since no reference to benchmarks for these problems were found in the literature. We use the sequential parameter optimization toolbox for tuning the parameters of the SA algorithm. We compare our results with those obtained by the OV-MDT algorithm that uses the obstacle value to sort the edges in the constructive process. This is the only available algorithm found in the literature. Through the experimental evaluation and statistical analysis, we assess the performance of the proposed algorithms using this operator.

Approximations on Minimum Weight Triangulations and Minimum Weight Pseudo-Triangulations Using Ant Colony Optimization Metaheuristic

Globally optimal triangulations and pseudo-triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) and Minimum Weight Pseudo-Triangulation (MWPT) problems of a given set of n points in the plane. This paper shows how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality triangulations and pseudo-triangulations of minimum weight. For the experimental study presented here we have created a set of instances for MWT and MWPT problems since no reference to benchmarks for these problems were found in the literature. Through the experimental evaluation, we assess the applicability of the ACO metaheuristic for MWT and MWPT problems considering greedy and Simulated Annealing algorithms.

Using ACO Metaheuristic for MWT Problem

Globally optimal triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) problem of a given set of n points in the plane. This paper shows how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality triangulations. For the experimental study we have created a set of instances for MWT problem since no reference to benchmarks for these problems were found in the literature. Through the experimental evaluation, we assess the applicability of the ACO metaheuristic for MWT problem.

Approximations on Minimum Weight Pseudo-triangulation Problem Using Ant Colony Optimization Metaheuristic

In this work, we consider the Minimum Weight Pseudo-Triangulation (MWPT) problem of a given set of

Adaptive clustering with artificial ants

n

Globally optimal triangulations of minimum weight using Ant Colony Optimization metaheuristic

points in the plane. Globally optimal pseudo-triangulations with respect to the

Estudio de la aplicabilidad de un enfoque evolutivo para la descomposición en Suma de Minkowski.

weight

Algoritmos genéticos para la descomposición en Sumas de Minkowski

, as optimization criteria, are difficult to be found by deterministic methods, since no polynomial algorithm is known. We show how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality pseudo-triangulations of minimum weight. We present the experimental and statistical study based on our own set of instances since no reference to benchmarks for these problems were found in the literature. Throughout the experimental evaluation, we appraise the ACO metaheuristic performance for MWPT problem.