A Bio-Inspired Algorithm for Searching Relationships in Social Networks

Proceedings of: Third International Conference on Computational Aspects of Social Networks (CASoN).Took place 2011, October,19-21 , in Salamanca (Sapin).The event Web site is http://www.mirlabs.net/cason11/Nowadays the Social Networks are experiencing a growing importance. The reason of this is that they enable the information exchange among people, meeting people in the same field of work or establishing collaborations with other research groups. In order to manage social networks and to find people inside them, they are usually represented as graphs with persons as nodes and relationships between them as edges. Once this is done, establishing contact with anyone involves searching the chain of people to reach him/her, that is, the search of the path inside the graph which joins two nodes. In this paper, a new algorithm based on nature is proposed to realize this search: SoS-ACO (Sense of Smell - Ant Colony Optimization). This algorithm improves the classical ACO algorithm when it is applied in huge graphs.This study was funded through a competitive grant awarded by the Spanish Ministry of Education and Science for the THUBAN Project (TIN2008-02711) and through MA2VICMR consortium (S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government.Publicad

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Ant colony optimisation-based radiation pattern manipulation algorithm for electronically steerable array radiator antennas

A new algorithm for manipulating the radiation pattern of Electronically Steerable Array Radiator Antennas is proposed. A continuous implementation of the Ant Colony Optimisation (ACO) technique calculates the optimal impedance values of reactances loading different parasitic radiators placed in a circle around a centre antenna. By proposing a method to obtain a suitable sampling frequency of the radiation pattern for use in the optimisation algorithm and by transforming the reactance search space into the search space of associated phases, special care was taken to create a fast and reliable implementation, resulting in an approach that is suitable for real-time implementation. The authors compare their approach to analytical techniques and optimisation algorithms for calculating these reactances. Results show that the method is able to calculate near-optimal solutions for gain optimisation and side lobe reduction

Using the ACO algorithm for path searches in social networks

The original publication is available at www.springerlink.comOne of the most important types of applications currently being used to share knowledge across the Internet are social networks. In addition to their use in social, professional and organizational spheres, social networks are also frequently utilized by researchers in the social sciences, particularly in anthropology and social psychology. In order to obtain information related to a particular social network, analytical techniques are employed to represent the network as a graph, where each node is a distinct member of the network and each edge is a particular type of relationship between members including, for example, kinship or friendship. This article presents a proposal for the efficien solution to one of the most frequently requested services on social networks; namely, taking different types of relationships into account in order to locate a particular member of the network. The solution is based on a biologically-inspired modificatio of the ant colony optimization algorithm.This study was funded through a competitive grant awarded by the Spanish Ministry of Education and Science for the THUBAN Project (TIN2008-02711) and through MA2VICMR consortium (S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government.Publicad

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance