Examinando por Autor "Olivares, Rodrigo"

Mostrando 1 - 8 de 8
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    A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models
    (MDPI, 2021) Castillo, Mauricio; Soto, Ricardo; Crawford, Broderick; Castro, Carlos; Olivares, Rodrigo
    Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.
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    A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods
    (Elsevier, 2021) Olivares, Rodrigo; Muñoz, Francisco; Riquelme, Fabián
    The influence maximization problem (IMP) is one of the most important topics in social network analysis. It consists of finding the smallest seed of users that maximizes the influence spread in a social network. The main influence spread models are the linear threshold model (LT-model) and the independent cascade model (IC-model). These models have mainly been treated by using the single-objective paradigm which covers just one perspective: maximize the influence spread starting by given seed size, or minimize the seed set to reach a given number of influenced nodes. Sometimes, this minimization problem has been called the least cost influence problem (LCI). In this work, we propose a new optimization model for both perspectives under conflict, through the LT-model, by applying a binary multi-objective approach. Swarm intelligence methods are implemented to solve our proposal on real networks. Results are promising and suggest that the new multi-objective solution proposed can be properly solved in harder instances.
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    A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
    (MDPI, 2021) Caselli, Nicolás; Soto, Ricardo; Broderick, Crawford; Valdivia, Sergio; Olivares, Rodrigo
    Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.
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    A Systematic approach to improve Support Vector Machine applied to ultrasonic guided wave spectrum image classification
    (IEEE, 2021) Miranda, Diego; Olivares, Rodrigo; Muñoz, Roberto; Jean-Gabriel Minonzio
    Osteoporosis is a skeletal disorder characterized by low bone mass, which compromises its resistance and increases the risk of fractures, and is a widespread problem worldwide. Currently, the gold standard for assessing fracture risk is the measurement of the areal bone mineral density with Dual-Energy X-ray Absorptiometry. Several ultrasound techniques have been presented as alternatives. It has been shown that the estimation of cortical thickness and porosity, obtained by Bi-Directional Axial Transmission, are associated with non-traumatic fractures in postmenopausal women. Cortical parameters were derived from the comparison between experimental and theoretical guided modes. However, this model-based inverse approach tends to fail for the patients associated with poor guided mode information. A recent study has shown the potential of an automatic classification tool, Support Vector Machine, to analyze guided wave spectrum images independently of any waveguide model. The aim of this study is to explore how the classification accuracy varies with the number of features. Optimization was done using the Particle Swarm Optimization algorithm, while adjustment was made considering age, body mass index, and cortisone intake. The results show that adjusting the data and optimizing the parameters improved classification. Moreover, the number of features was reduced from 32 to 15, with 73.5% accuracy comparable to the gold standard.
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    Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods
    (Tech Science Press, 2022) Riquelme, Fabián; Olivares, Rodrigo; Muñoz, Francisco; Molinero, Xavier; Serna, Maria
    An influence game is a simple game represented over an influence graph (i.e., a labeled, weighted graph) on which the influence spread phenomenon is exerted. Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis, decision-systems, voting systems, and collective behavior. The exact calculation of several of these properties and parameters is computationally hard, even for a small number of players. Two examples of these parameters are the length and the width of a game. The length of a game is the size of its smaller winning coalition, while the width of a game is the size of its larger losing coalition. Both parameters are relevant to know the levels of difficulty in reaching agreements in collective decision-making systems. Despite the above, new bio-inspired metaheuristic algorithms have recently been developed to solve the NP-hard influence maximization problem in an efficient and approximate way, being able to find small winning coalitions that maximize the influence spread within an influence graph. In this article, we apply some variations of this solution to find extreme winning and losing coalitions, and thus efficient approximate solutions for the length and the width of influence games. As a case study, we consider two real social networks, one formed by the 58 members of the European Union Council under nice voting rules, and the other formed by the 705 members of the European Parliament, connected by political affinity. Results are promising and show that it is feasible to generate approximate solutions for the length and width parameters of influence games, in reduced solving time.
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    Human Behaviour Based Optimization Supported With Self-Organizing Maps for Solving the S-Box Design Problem
    (IEEE, 2021) Soto, Ricardo; Crawford, Broderick; González Molina, Francisco; Olivares, Rodrigo
    The cryptanalytic resistance of modern block and stream encryption systems mainly depends on the substitution box (S-box). In this context, the problem is thus to create an S-box with higher value of nonlinearity because this property can provide some degree of protection against linear and differential cryptanalysis attacks. In this paper, we design a scheme built on a human behavior-based optimization algorithm, supported with Self-Organizing Maps to prevent premature convergence and improve the nonlinearity property in order to obtain strong 8 ×8 substitution boxes. The experiments are compared with S-boxes obtained using other metaheuristic algorithms such as Ant Colony Optimization, Genetic Algorithm and an approach based on chaotic functions and show that the obtained S-boxes have good cryptographic properties. The obtained S-box is investigated against standard tests such as bijectivity, nonlinearity, strict avalanche criterion, bit independence criterion, linear probability and differential probability, proving that the proposed scheme is proficient to discover a strong nonlinear component of encryption systems.
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    Selection of Bone fragility-Related Features Obtained with Bi-Directional Axial Transmission, Through a Machine Leaming Strategy
    (IEEE, 2021) Miranda, Diego; Olivares, Rodrigo; Munoz, Roberto; Minonzio, Jean-Gabriel
    Osteoporosis is a widespread public health problem worldwide, characterized by low bone mass, which compromises strength and increases the risk of fracture. Currently, the gold standard for assessing fracture risk is measurement of areal bone mineral density with dual-energy X-ray absorptiometry (DXA). Several ultrasound techniques, such as Bi-Directional Axial Transmission (BDAT) have been presented as alternatives. For the first studies, classification between fractured and non fractured patients was based on classical ultrasonic parameters, such as velocities or cortical thickness and porosity, obtained from an inverse problem. Recently, novel parameters obtained from structural analysis guided wave spectrum images (GWSI) have been introduced. The aim of this study is to merge both points of view and explore which parameters are the most important to obtain a robust classification using a machine learning approach. This study uses the same set of patients used in previous studies with 195 patients associated with 8 ultrasonic parameters and 3 clinical factors (age BMI and cortisone intake). In addition, each patient corresponds to 10 GWSI, from which 32 parameters of structural analysis are extracted per image, leading to a total of 43 features per image. The dataset was divided into 70% of patients (n = 136) as training and 30% as testing (n = 59). The distribution of patients was adjusted for age and target class. The accuracy was calculated for an increased number of features, which ranking was obtained using Recursive Feature Elimination (RFE). The highest accuracy of 71% is obtained with the optimized parameters and a combination between 22 and 25 features. These result, comparable to femoral DXA (AUC = 0.71, adjusted linear regression), opens perspective towards robust detection of patients at risk of fracture with ultrasound.
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    Social influence under improved multi-objective metaheuristics
    (ACS, 2021) Riquelme, Fabian; Muñoz, Francisco; Olivares, Rodrigo
    The influence maximization problem (IMP) and the least cost influence problem (LCI) are two relevant and widely studied problems in social network analysis. The first one consists of maximizing the influence spread in a social network, starting with a given seed size of actors; the second one consists of minimizing the seed set to reach a given number of influenced nodes. Recently, both problems have been studied together with a multi-objective metaheuristic approach. In this work, diffusion filter restrictions based on the network topology are proposed to reduce the search space and thus improving the convergence speed of the solutions. This proposal allows increasing the quality of the results. As the influence spread model, the Linear Threshold model will be used. The solution is tested in three social networks of different sizes, finding promising improvements in harder instances.