Um GRASP Híbrido com Reconexão por Caminhos e Mineração de Dados

A exploração de metaheurísticas híbridas – combinação de metaheurísticas com conceitos e processos de outras áreas – vem sendo uma importante linha de pesquisa em otimização combinatória. Neste trabalho, propõe-se uma versão híbrida da metaheurística GRASP que incorpora a técnica de reconexão por caminhos e um módulo de mineração de dados. Experimentos computacionais mostraram que a combinação da técnica de reconexão por caminhos com mineração de dados contribuiu para que o GRASP encontrasse soluções melhores em um menor tempo computacional. Outra contribuição deste trabalho é a aplicação dessa proposta híbrida ao problema de síntese de redes a 2-caminhos, que proporcionou encontrar melhores soluções para esse problema

A Branch-and-Cut and MIP-based heuristics for the Prize-Collecting Travelling Salesman Problem

The Prize Collecting Traveling Salesman Problem (PCTSP) represents a generalization of the well-known Traveling Salesman Problem. The PCTSP can be associated with a salesman that collects a prize in each visited city and pays a penalty for each unvisited city, with travel costs among the cities. The objective is to minimize the sum of the costs of the tour and penalties, while collecting a minimum amount of prize. This paper suggests MIP-based heuristics and a branch-and-cut algorithm to solve the PCTSP. Experiments were conducted with instances of the literature, and the results of our methods turned out to be quite satisfactory

Reactive GRASP for the Prize-collecting Covering Tour Problem

This paper presents a Greedy Randomized Adaptive Search Procedure (GRASP) for the Prize-Collecting Covering Tour Problem (PCCTP), which is the problem of finding a route for traveling teams that provide services to communities geographically distant from large urban locations. We devised a novel hybrid heuristic by combining a reactive extension of the GRASP with Random Variable Neighborhood Search (VND) meta-heuristic for the purpose of solving the PCCTP. Computational experiments were conducted on a PCCTP benchmark from the literature, and the results demonstrate our approach provides a significant improvement in solving PCCTP and comparable with the state-of-the-art, mainly regarding the computational processing time

Um GRASP Híbrido com Reconexão por Caminhos e Mineração de Dados

A exploração de metaheurísticas híbridas – combinação de metaheurísticas com conceitos e processos de outras áreas – vem sendo uma importante linha de pesquisa em otimização combinatória. Neste trabalho, propõe-se uma versão híbrida da metaheurística GRASP que incorpora a técnica de reconexão por caminhos e um módulo de mineração de dados. Experimentos computacionais mostraram que a combinação da técnica de reconexão por caminhos com mineração de dados contribuiu para que o GRASP encontrasse soluções melhores em um menor tempo computacional. Outra contribuição deste trabalho é a aplicação dessa proposta híbrida ao problema de síntese de redes a 2-caminhos, que proporcionou encontrar melhores soluções para esse problema

A BRKGA-based matheuristic for the maximum quasi-clique problem with an exact local search strategy

Given a graph G = (V, E) and a threshold γ ∈ (0, 1], the maximum cardinality quasi- clique problem consists in finding a maximum cardinality subset C* of the vertices in V such that the density of the graph induced in G by C* is greater than or equal to the threshold γ. This problem has a number of applications in data mining, e.g., in social networks or phone call graphs. We propose a matheuristic for solving the maximum cardinality quasi-clique problem, based on the hybridization of a biased random-key genetic algorithm (BRKGA) with an exact local search strategy. The newly proposed approach is compared with a pure biased random-key genetic algorithm, which was the best heuristic in the literature at the time of writing. Computational results show that the hybrid BRKGA outperforms the pure BRKGA

sequential and

Exploiting run time distributions to compar

New Benchmark Instances for the Steiner Problem in Graphs

We propose in this work 50 new test instances for the Steiner problem in graphs. These instances are characterized by large integrality gaps (between the optimal integer solution and that of the linear programming relaxation) and symmetry aspects which make them harder to both exact methods and heuristics than the test instances currently in use for the evaluation and comparison of existing and newly developed algorithms. Our computational results indicate that these new instances are not amenable to reductions by current preprocessing techniques and that not only do the linear programming lower bounds show large gaps, but they are also hard to be computed. State-of-the-art heuristics, which found optimal solutions for almost all test instances currently in use, faced much more difficulties for the new instances. Fewer optimal solutions were found and the numerical results are more discriminant, allowing a better assessment of the effectiveness and the relative behavior of different heuristics