Laura Cruz

Vertical Fragmentation and Allocation in Distributed Databases with Site Capacity Restrictions Using the Threshold Accepting Algorithm

This paper presents an extension of the DFAR mathematical optimization model, which unifies the fragmentation, allocation and dynamical migration of data in distributed database systems. The extension consists of the addition of a constraint that models the storage capacity of network sites. This aspect is particularly important in large databases, which exceed the capacity of one or more sites. The Threshold Accepting Algorithm is a variation of the heuristic method known as Simulated Annealing, and it is used for solving the DFAR model. The paper includes experimental results obtained for large test cases.

A Statistical Approach for Algorithm Selection

This paper deals with heuristic algorithm characterization, which is applied to the solution of an NP-hard problem, in order to select the best algorithm for solving a given problem instance. The traditional approach for selecting algorithms compares their performance using an instance set, and concludes that one outperforms the other. Another common approach consists of developing mathematical models to relate performance to problem size. Recent approaches try to incorporate more characteristics. However, they do not identify the characteristics that affect performance in a critical way, and do not incorporate them explicitly in their performance model. In contrast, we propose a systematic procedure to create models that incorporate critical characteristics, aiming at the selection of the best algorithm for solving a given instance. To validate our approach we carried out experiments using an extensive test set. In particular, for the classical bin packing problem, we developed models that incorporate the interrelation among five critical characteristics and the performance of seven heuristic algorithms. As a result of applying our procedure, we obtained a 76% accuracy in the selection of the best algorithm.

Self-Tuning Mechanism for Genetic Algorithms Parameters, an Application to Data-Object Allocation in the Web

In this paper, a new mechanism for automatically obtaining some control parameter values for Genetic Algorithms is presented, which is independent of problem domain and size. This approach differs from the traditional methods which require knowing first the problem domain, and then knowing how to select the parameter values for solving specific problem instances. The proposed method is based on a sample of problem instances, whose solution permits to characterize the problem and to obtain the parameter values.To test the method, a combinatorial optimization model for data-objects allocation in the Web (known as DFAR) was solved using Genetic Algorithms. We show how the proposed mechanism permits to develop a set of mathematical expressions that relates the problem instance size to the control parameters of the algorithm. The experimental results show that the self-tuning of control parameter values of the Genetic Algorithm for a given instance is possible, and that this mechanism yields satisfactory results in quality and execution time. We consider that the proposed method principles can be extended for the self-tuning of control parameters for other heuristic algorithms.

Artificial Societies and Social Simulation using Ant Colony, Particle Swarm Optimization and Cultural Algorithms

The proposal of this chapter is to explain the implementation of collective intelligent techniques to improve results in artificial societies and social simulation using diverse concepts such as argumentation, negotiation and reputation models to improve social simulation of artificial societies implementing dioramas, and multivariable analysis in different application domains for example Logistics. These techniques will be useful for answering diverse queries after gathering general information about a given topic. This kind of collective intelligence will be characterized by: ant colony, particle swarm optimization, and cultural algorithms, each one of these implementing diverse models or agents for simulate a social behaviour. Intelligent agents are used to obtain information to take decisions that try to improve the heuristic optimization needed in different application and fields of knowledge. First, in section 1 of this paper, we approach different concepts related with Artificial Societies and Social Simulation using different strategies to analyze and model the necessary information to support the correct decisions of the evolving models. In other sections we explain the way to generate a specific behaviour with collective-intelligence techniques –ant colony (section 2), particle swarm optimization (section 3), and cultural algorithms (section 4). In section 5 we apply this knowledge in diverse fields and application domains that needs a heuristic optimization and the more innovative perspectives of each technique. In

Handling of Synergy into an Algorithm for Project Portfolio Selection

Public and private organizations continuously invest on projects. With a number of candidate projects bigger than those ones that can be funded, the organization faces the problem of selecting a portfolio of projects that maximizes the expected benefits. The selection is made on the evaluation of project groups and not on the evaluation of single projects. However, there is a factor that must be taken account, since it can significantly change the evaluation of groups: synergy. This is that two or more projects are complemented in a way that generates an additional benefit to they already own individually. Redundancy, a special case of synergy, occurs when two or more projects cannot be financed simultaneously. Both features add complexity to the evaluation of project groups. This article presents an evaluation of the two most used alternatives for handling synergy, in order to incorporate it into an ant-colony metaheuristic for solving project portfolio selection.

Project Ranking-Based Portfolio Selection Using Evolutionary Multiobjective Optimization of a Vector Proxy Impact Measure

Selecting project portfolios Decision-Maker usually starts with limited information about projects and portfolios. One of the challenges involved in analyzing, searching and selecting the best portfolio is having a method to evaluate the impact of every project and portfolio in order to compare them. This paper develops a model for composing publicoriented project portfolios. Information concerning the quality of the projects is in the form of a project-ranking, which can be obtained by the application of a proper multi-criteria method; however the ranking does not assume an appropriate evaluation. A best portfolio is primarily found through a multi-objective optimization that regards the impact indicators that reflect the quality of the projects in the portfolio and competent portfolios’ cardinalities. Overall good solutions are obtained by developing an evolutionary method, which is found to perform well in some test examples.

Hyperheuristic for the parameter tuning of a bio-inspired algorithm of query routing in p2p networks

The computational optimization field defines the parameter tuning problem as the correct selection of the parameter values in order to stabilize the behavior of the algorithms. This paper deals the parameters tuning in dynamic and large-scale conditions for an algorithm that solves the Semantic Query Routing Problem (SQRP) in peer-to-peer networks. In order to solve SQRP, the HH_AdaNAS algorithm is proposed, which is an ant colony algorithm that deals synchronously with two processes. The first process consists in generating a SQRP solution. The second one, on the other hand, has the goal to adjust the Time To Live parameter of each ant, through a hyperheuristic. HH_AdaNAS performs adaptive control through the hyperheuristic considering SQRP local conditions. The experimental results show that HH_AdaNAS, incorporating the techniques of parameters tuning with hyperheuristics, increases its performance by 2.42% compared with the algorithms to solve SQRP found in literature.

Multicriteria optimization of interdependent project portfolios with 'a priori' incorporation of decision maker preferences

One of the most important management issues lies in determining the best portfolio of a given set of investment proposals. This decision involves the pursuit of multiple criteria, and has been commonly addressed by implementing a two-phase procedure whose first step identifies the efficient solution space. In this paper we introduce our algorithm called Non-Outranked Ant Colony Optimization (NO-ACO) that optimizes portfolios with interprojects interactions whilst takes into account the DM’s preferences by incorporating a priori preferences articulation. Experimental tests show the advantages of our proposal over the two-phase approach. Also, NO-ACO performed particularly well for problems with high dimensionality.

A causal approach for explaining why a heuristic algorithm outperforms another in solving an instance set of the bin packing problem

The problem of algorithm selection for solving NP problems arises with the appearance of a variety of heuristic algorithms. The first works claimed the supremacy of some algorithm for a given problem. Subsequent works revealed that the supremacy of algorithms only applied to a subset of instances. However, it was not explained why an algorithm solved better an instances subset. In this respect, this work approaches the problem of explaining through causal modeling the interrelations between instances characteristics and the inner workings of algorithms. For validating the results of the proposed approach, a set of experiments was carried out in a study case of the Tabu Search algorithm applied to the Bin Packing problem. Finally, the proposed approach can be useful for redesigning the logic of heuristic algorithms and for justifying the use of an algorithm to solve an instance subset. This information could contribute to algorithm selection for NP-hard problems.

SoapFS: a multiplatform file system

Distributed computer applications usually need processes that allow them to retrieve, store, and share data in a suitable way. As a consequence, file systems become a basic concern for these kinds of applications. Typical file systems have been designed on a computer network infrastructure whose communication and data exchange support is homogeneous. The Internet encourages the construction of file systems, which deal with heterogeneous computer platforms. This work presents SoapFS, a file system that is able to manage information in a heterogeneous environment such as the Internet. This is possible because it is based on technologies like RPC-XML and SOAP. This paper shows how a set of functions available in SoapFS allows the development of robust and heterogeneous distributed applications that involve remote data retrieval and storage. SoapFS shows competitive performance compared with popular file systems and it can connect different file systems working together like a one virtual file system.