Claudia Gómez Santillán

A cultural algorithm for the urban public transportation

In the last years the population of Leon City, located in the state of Guanajuato in Mexico, has been considerably increasing, causing the inhabitants to waste most of their time with public transportation As a consequence of the demographic growth and traffic bottleneck, users deal with the daily problem of optimizing their travel so that to get to their destination on time To give a solution to this problem of obtaining an optimized route between two points in a public transportation, a method based on the cultural algorithms technique is proposed Cultural algorithms are used in the generated knowledge in a set of time periods for a same population, using a belief space These types of algorithms are a recent creation The proposed method seeks a path that minimizes the time of traveling and the number of transfers The results of the experiment show that the technique of the cultural algorithms is applicable to these kinds of multi-objective problems.

A Survey of Decomposition Methods for Multi-objective Optimization

The multi-objective optimization methods are traditionally based on Pareto dominance or relaxed forms of dominance in order to achieve a representation of the Pareto front. However, the performance of traditional optimization methods decreases for those problems with more than three objectives to optimize. The decomposition of a multi-objective problem is an approach that transforms a multi-objective problem into many single-objective optimization problems, avoiding the need of any dominance form. This chapter provides a short review of the general framework, current research trends and future research topics on decomposition methods.

NAS Algorithm for Semantic Query Routing Systems in Complex Networks

The modern distributed systems are acquiring a great importance in our daily lives. Each day more transactions are conducted through devices which perform queries that are dependent on the reliability, availability and security of distributed applications. In addition, those systems show great dynamism as a result of the extremely complex and unpredictable interactions between the distributed components, making it practically impossible to evaluate their behavior. In this paper, we evaluate the performance of the NAS algorithm (Neighboring-Ant Search), which is an algorithm for distributed textual query routing based on the Ant Colony System metaheuristic and SemAnt algorithm, improved with a local topological characterization metric and a classic local exploration method called lookahead, with the aim of improving the performance of the distributed search. Our results show that including local information like the topological metric and the exploration method in the Neighboring-Ant Search algorithm improves its performance 40%, in terms of the number of hops needed to locate a set of resources in a scale-free network.

Impact of dynamic growing on the internet degree distribution

A great amount of natural and artificial systems can be represented as a complex network, where the entities of the system are related of non-trivial form. Thus, the network topology is the pattern of the interactions between entities. The characterization of complex networks allows analyzing, classifying and modeling the topology of complex networks. The degree distribution is a characterization function used in the analysis of complex networks. In this work a comparative study of the degree distribution for three different instances of the Internet was carried out, with information about the interconnection of domains. The Internet has a degree distribution power-law, that is, it has a great amount of weakly connected domains while a few domains have a great number of connections. Our results show that Internet has a dynamic growing maintaining the degree distribution power-law through the time, independently of the growth in the number of domains and its connections.

Improving the performance of heuristic algorithms based on causal inference

Causal inference can be used to construct models that explain the performance of heuristic algorithms for NP-hard problems. In this paper, we show the application of causal inference to the algorithmic optimization process through an experimental analysis to assess the impact of the parameters that control the behavior of a heuristic algorithm. As a case study we present an analysis of the main parameters of one state of the art procedure for the Bin Packing Problem (BPP). The studies confirm the importance of the application of causal reasoning as a guide for improving the performance of the algorithms.

Statistical Selection of Relevant Features to Classify Random, Scale Free and Exponential Networks

In this paper a statistical selection of relevant features is presented. An experiment was designed to select relevant and not redundant features or characterization functions, which allow quantitatively discriminating among different types of complex networks. As well there exist researchers given to the task of classifying some networks of the real world through characterization functions inside a type of complex network, they do not give enough evidences of detailed analysis of the functions that allow to determine if all are necessary to carry out an efficient discrimination or which are better functions for discriminating. Our results show that with a reduced number of characterization functions such as the shortest path length, standard deviation of the degree, and local efficiency of the network can discriminate efficiently among the types of complex networks treated here.

Multiobjective Optimization Approach for Preference-Disaggregation Analysis Under Effects of Intensity

A widely used approach in Multicriteria Decision Aid is the Preference Disaggregation Analysis (or PDA). This is an indirect approach used to characterize the decision process of a Decision Maker (or DM). By means of a limited set of examples (called a reference set) provided by the DM, the PDA approach estimates the parameter values of a preference model that is characterized by the DM. This paper proposes a new optimization model for PDA, and its solution through an evolutionary algorithm. The novel features in the definition of the model include the use of the effect of the intensity (i.e. the variations among the criteria values used to evaluate decision alternatives), and new ways to combine the number of consistencies and inconsistencies with respect to the reference set. Through an experimental design performed to evaluate the fitness of the new model, it was corroborated its effectiveness to fit the DM preferences, and also it showed comparable results with that provided by an state-of-the-art strategy.

A Comparison Between Memetic Algorithm and Seeded Genetic Algorithm for Multi-objective Independent Task Scheduling on Heterogeneous Machines

This chapter is focused on the problem of scheduling independent tasks on heterogeneous machines. The main contributions of our work are the following: a linear programming model to compute energy consumption for the execution of independent tasks on heterogeneous clusters, a constructive heuristic based on local search, and a new benchmark set. To assess our approach we compare the performance of two solution methods: a memetic algorithm, based on population search and local search, and a seeded genetic algorithm, based on NSGA-II. A Wilcoxon rank-sum test shows significant differences in the diversity of solutions found but not in hypervolume. The memetic algorithm gets the best diversity for a bigger instance set from the state of the art.

Simplification of Decision Rules for Recommendation of Projects in a Public Project Portfolio

In this paper, we propose the use of decision rules to aid in the recommendation of a portfolio of public projects. A decision table indicates what decisions should be made when the condition attributes are satisfied. Projects can be modeled as decision tables, where the characteristics of the projects are condition attributes and the qualification of each project is the decision attribute. Reducing the decision rules, we can give a simple explanation of why a certain project has its qualification; this simplification is a useful procedure because most decision problems can be formulated in a decision table. Public portfolio problem, due to its nature, has been approached by multi-criteria algorithms, which generate a set of solutions in the Pareto frontier. The selection of a portfolio depends on the decision maker, so the simplified decision rules can help him/her to analyze why a project have been added to a certain portfolio and justify the final selection.

A Decision Support System Framework for Public Project Portfolio Selection with Argumentation Theory

In this chapter, we propose a framework for a Decision Support System (DSS) to aid in the selection of public project portfolios. Organizations are investing continuously and simultaneously in projects, however, they face the problem of having more projects than resources to implement them. Public projects are designed to favor society. Researches have commonly addressed the public portfolio selection problem with multicriteria algorithms, due to its high dimensionality. These algorithms focus on identifying a set of solutions in the Pareto frontier. However, the selection of the solution depends on the changing criteria of the decision maker (DM). A framework to support the DM is presented; it is designed to help the DM by a dialogue game to select the best portfolio in an interactive way. The framework is based on the argumentation theory and a friendly user interface. The dialogue game allows the DM to ask for justification on the project portfolio selection.