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ACO is a metaheuristic inspired in the behavior of natural ant colonies to solve combinatorial optimization problems, based on simple agents that work cooperatively communicating by artificial pheromone trails. In this paper a model to solve the municipal waste collection problem by containers is presented, which applies a concept of partial collection sequences that must be joined to minimize the total collection distance. The problem to join the partial collection sequences is represented as a TSP, which is solved by an ACO algorithm. Based on the literature, algorithm parameters are experimentally calibrated and range of variations that represents good average solutions are recommended. The model is applied to a waste collection sector of the San Pedro de la Paz commune in Chile, obtaining recollection routes with less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach. [ABSTRACT FROM AUTHOR]

To improve the quality of service in cell systems, techniques of Dynamic Assignment Channels are used to reduce the probability of blocking calls. In this paper an algorithm of assignment channels is required to reduce the probability of blocking, extending the system capacity to add new calls. This is obtained by using an interchange of channels of current calls and reducing the restrictions of electromagnetic compatibility. According to this, the algorithm has to provide solutions of commitments between two different objectives; on the one hand reduce the amount of assignments, and on the other to do the same with the level of interference due to the flexible restrictions of electromagnetic compatibility. A heuristic focus based on the evolution paradigm is presented to solve the multi-objective optimization problem. Two evolutionary multi-objective algorithms (SPEAII y NSGA II) are compared by the use of two different metrics. The results show that the proposed methodology can be applied to different generations of mobile communication systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel algorithm to reconfigure an electric power distribution network (EPDN) minimizing its non-supplied energy (NSE). The EPDN is modeled using graph theory and the NSE is recursively formulated in terms of the reliability parameters of the EPDN. Based on this mathematical model, we transform the original optimization problem into the graph theory problem of finding the minimum spanning tree (MST) of a given graph, which models the EPDN. The distance metric employed by the searching algorithm is the NSE. In order to efficiently find the MST, Prim's algorithm is employed due to is greedy search behavior. In addition, a backtracking algorithm is used to check the MST obtained. The backtracking algorithm analyzes all the candidate topologies that were randomly discarded during the decision process. The performance of the optimization algorithm is evaluated by using testing systems and two actual EPDNs. [ABSTRACT FROM AUTHOR]

In this paper, we develop a fuzzy analytical model, which solves a multiobjective problem in multicast transmission with static trees over GMPLS networks. A model proponed by Donoso [4] was used. The multiobjective problem is solved through metaheuristic wit h evolutionary algorithms, in this case SPEA2 solving a NP-Hard problem in polynomial time. Additionally, a Fuzzy Evolutionary Algorithm based on SPEA2 is implemented. Finally, the comparison between FSPEA2 and SPEA2 is realized and we are giving some conclusions. [ABSTRACT FROM AUTHOR]

In this paper, the design of a genetic algorithm that modifies and recalculates the values those describe the membership functions for a fuzzy controller, has showed. The controller is applied in a bio-film fermentative process to obtain an improvement in the dichromate's production (DCM). The membership functions act over the concentration of substrate in the aeration tank so that produces the homogenous double saturation kinetic of the DCM, and this is increased, obtaining an improvement in their production. This is shown by development of the numerical algorithm. [ABSTRACT FROM AUTHOR]

Rational Choice Theory has been used extensively in electoral studies. This theory suggests that a voter chooses the political party that is closer to his or her ideology. However, there is scant empirical evidence on which issues are considered in a particular election and their relative weight on the voter's calculations. This paper presents a new model for electoral studies using mathematical optimization. It is shown that this model allows for better explanations of electoral outcomes, assigning with a clear methodology the weight that each issue has used on voter's choice. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization method which is an alternative solution for multicast networks load balancing, using a software implementation of the Simulated Annealing heuristic. The method minimize four basic parameters necessaries for guarantee the quality of service (QoS) in multicast transmissions, these are: end-to-end delay, maximum link utilization, bandwidth consumption and hop count. The results obtained by the heuristic will be compared with the results hurtled by the mathematical pattern proposed in previous investigations. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of evolutionary algorithms for multi objective optimization, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA-II) is presented. For this analysis, is taken as reference an optimization problem in a multicast data network, which has as objective functions the hop count and transmission delay. The algorithms performance is compared in tree different networks. Moreover, the model for two of this topologies using GAMS tool is resolved and results are compared with the NSGA-II and SPEA-II algorithms proposed. Problem results show the algorithms performance in their solution. [ABSTRACT FROM AUTHOR]

The potential field inverse problems are difficult to solve by means of automatic methods, one major reason for this is the inability of conventional procedures to locate the globally optimal set of parameters. This paper establish clearly the nature of the multiple optima problem for the research to inversion of potential field data in the six parameters model and investigates the consistency with three global optimization methods. The Multistart Simplex (MSX) method, a Genetic algorithm combined with Simplex (GSX) and the Shuffled Complex Evolution (SCE) method. All of them are able to find the optimal parameters set during the inversion of potential field data for a six-parameter model. In 100 independent trials of each algorithm the SCE procedure appears to be about 3 times more efficient than MSX procedure and twice time more efficient than GSX. The GSX procedure appears to be about 2 times more efficient than MSX. [ABSTRACT FROM AUTHOR]

In this paper we investigate the electromagnetic response of a two-dimensional target buried in a half space conducting earth. The electromagnetic source is an infinite wire. We develop a numerical program to find information about parameters like deep, conductivity contrast and target's dimensions. This information is obtained through the imaginary and real part of the response signal. Furthermore, we show the theory of the scaling modeling in electromagnetic sounding to compare the theoretical results with laboratory measurements. [ABSTRACT FROM AUTHOR]

In this paper, we compare, from a theoretical and a practical viewpoint, two settings of a certain tax-programming problem and the algorithms proposed for solving it. This problem may be modeled as a parametric quadratic-programming problem having lower and upper bounds on the variables. The objective function is a quadratic function whose Hessian matrix is diagonal and positive. A term is added that penalizes not to achieving a certain goal. This penalization can be formulated either as an absolute or as a quadratic deviation. [ABSTRACT FROM AUTHOR]

"Multidimensional Scaling" is the name of a set of procedures with the objective of data representation in a graphical plot when some information about similarities between objects is known. The history of these procedures began with Torgeson's work in 1952 who gave the preliminary ideas. Since then many investigators have developed other works with one only aim: to obtain a euclidean representation most similar to the starting proximities between objects. In this paper we show the main purposes and some essential concepts about this thematic. [ABSTRACT FROM AUTHOR]

A study of the optimal pipe diameter selection, when in the system the pumped fluid is non newtonian, is made in this paper. For this case, the most used correlations are given. The application of this correlation is showed through two illustrative examples. [ABSTRACT FROM AUTHOR]

This paper introduces the Strategy of Experimental Optimization (SEO) through a Scheme Generalized of Learning (SGL) that facilitates the understanding of the necessary steps in order to arrive at a point of maximum or minimum answer depending on the particular situation being studied. Important considerations that should be kept in mind in each phase of the SEO are exposed in relation to the taking of decisions as well as avoiding frequent mistakes on carrying out experimental design. As an illustrative example, a basic extraction stage of sodium alginate obtention process is presented, emphasizing in the interpretation of mathematical models and response surfaces. [ABSTRACT FROM AUTHOR]

In the present paper a brief description of the used methodical for the preparation of a macroscopic few group constant library is given. This library was elaborated with the aim of algorithmic optimization including gadolinium spectral effects. The main physical hypotheses are discussed, as well as the adopted way for performing calculations by means of spectral code WIMS-D/4. The parametrization model for library elaboration is also given. Conclusions regard as satisfactory the obtained results for VVER-440 type reactors. [ABSTRACT FROM AUTHOR]