Rogelio González Velázquez

An approximation method for the P-median problem: A bioinspired tabu search and variable neighborhood search partitioning approach

The bioinspired systems are presented as a set of models that are based on the behavior of some biological systems and how act. These models can be expressed in data mining and operations research where clustering is a recurrent technique used to solve the P-median and territorial design problems. At this point, we have solved the P-median problem with a partitioning approach with bioinspired aspects and Variable Neighborhood Search (VNS). In this work we have improved the basic VNS search strategy and we present a bioinspired partitioning algorithm with optimization by Tabu Search (TS). This clusteringpartitioning problem under a bioinspired connotation has been proposed after observing some characteristics in common between clustering and human behavior during conflict situations, where some characteristics have been modeled accordingly. Finally we present our progress from our VNS implementation to our TS proposal.

A Neighborhood Combining Approach in GRASP's Local Search for Quadratic Assignment Problem Solutions

In this paper we describe a study for the search of solutions of the combinatorial optimization problem Quadratic Assignment Problem (QAP) through the implementation of a Greedy Randomized Adaptive Procedure Search (GRASP) and have been compared with the best solutions known in the literature, obtaining robust results in terms of the value of the objective function and the execution time. Also a comparison with the ant algorithm is presented with the aim of comparing the meta-heuristic. The most important contribution of this paper is the use of the combination of different neighborhood structures in the GRASP improvement phase. The experiment was performed for a set of test instances available in QAPLIB. The QAP belongs to the Np-hard class whereby this approximation algorithm is implemented.

Bioinspired Tabu Search for Geographic Partitioning

The analytical observation of nature induces inspiration to propose new computational paradigms to create algorithms that solve optimization and artificial intelligence problems. The artificial vision allows establishing a problem with intelligent techniques from living systems. The bioinspired systems are presented as a set of models that are based on the behavior and the way of acting of some biological systems. These models can be expressed in data mining and operations research where the clustering is a recurrent technique in the P-median problem and territorial design. On this point, we have solved clustering problems using partitioning with bioinspired aspects and variable neighborhood search to approximate optimal solutions. In this work we have improved the search strategy: we present a bioinspired partitioning algorithm with optimization by tabu search (TS). This clustering problem under a bioinspired connotation has been proposed after observing some characteristics in common between clustering and human behavior in conflict situations, where some characteristics have been modeled.

A Location Allocation Model for a Territorial Design Problem with Dense Demand

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Design of Experiments for a multi-objective partitioning problem

In this paper we present a factorial statistical experiment for a combinatorial optimization problem bi-objective, which optimizes two functions in conflict: geometric compactness and homogeneity to variables of a population problem, it belongs to the area of design territory. Such problems invests its utmost in the biobjetivo grouping to build groups of areas under Partitioning properties where territorial partitions must be as compact and homogeneous as possible. the resolution of compromise between two objectives must be approached with a multi-objective technique to find non-dominated solutions which in turn form the set of solutions framed in a Pareto Front. A new method is proposed to find the set of solutions not dominated based on basic aspects of order theory, particularly Hasse diagram for the Minim and computational cost management has been incorporated the metaheuristic called Variable Neighborhood Search (VNS). Finally to calibrate the parameters of VNS has been done using a factorial experiment known as Box Benhken and Response Surfaces, thus we have achieved an ideal combination of parameters to obtain satisfactory solutions to the multiobjetivo problem.

TSP in Partitioning with Tabu Search

Solving Territorial Design problems implies grouping territorial units into k of groups with compactness and/or contiguity restrictions. However each group formed is often treated in accordance to a conflict of interest; one of them is the routing problem. In this work grouping geographical units is also known as classification by partitions, which is a well-known high complexity problem. This complexity requires reaching approximated partitioning solutions of the territory in a reasonable computing time; therefore we have chosen the tabu search metaheuristic because it has achieved very efficient results in several optimization problems. Once tabu search has returned a solution, we apply an exact algorithm to the elements of the partition, which solves a routing problem.

Partitioning with Variable Neighborhood Search: A bioinspired approach

The artificial vision allows us to reduce a problem by means of techniques that have obeyed the study of the intelligence of living systems. A well-known technique is data mining and pattern recognition, which are disciplines dependent of artificial intelligence that from some data, allow the acquisition of knowledge and in particular, within data mining, a great application in the field of bioinformatics has been found. What is more, the big and diverse expansion of the amount of data produced by problems related to biological behavior has generated the necessity of constructing precise algorithms of prediction and classification. The precision of classification algorithms can be affected by diverse factors, some of them considered generics in any automatic learning algorithm and, therefore, applicable to the distinct research areas. These factors are the ones that have received attention in the field of automatic learning and pattern recognition, where different clustering algorithms are observed, in particular the automatic classification or better known as classification by partitions. In this scenery, is important to discover an analogy about the way that some living beings form groups to survive in their environment finding an optimal sequence or structure or, that group their objects or belongings, against a classification by partitions algorithm. The partitioning is an NP-hard problem, thus the incorporation of approximated methods is necessary. The heuristic that we expose here is Variable Neighborhood Search (VNS) focusing in the way that this heuristic does the search of neighbor conditions by means of neighborhoods to get a satisfactory solution, just like some living beings usually do it when they try to adapt to a neighborhood close to theirs or to the current space. In this work, we focus on describing in a bioinspired way, a technique of data mining known as partitional grouping with the inclusion of VNS with the purpose of finding approximated solutions for a clustering problem.