Martínez Ríos, Félix Orlando
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Martínez Ríos, Félix Orlando
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Martínez Ríos, Félix Orlando
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fmartin
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ORCID
Scopus Author ID
24339038500
Researcher ID
N-7502-2018

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Now showing 1 - 10 of 34
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Golden Ratio Annealing for Satisfiability Problems Using Dynamically Cooling Schemes

2008 , Frausto-Solís, Juan , Martínez Ríos, Félix Orlando

Abstract Satisfiability (SAT) Problem is an NP-Complete problem which means no deterministic algorithm is able to solve it in a polynomial time. Simulated Annealing (SA) can find very good solutions of SAT instances if its control parameters are correctly tuned. SA can be tuned experimentally or by using a Markov approach; the latter has been shown to be the most efficient one. Moreover Golden Ratio (GR) is an unconventional technique used to solve many problems. In this paper a new algorithm named Golden Ratio for Simulated Annealing (GRSA) is presented; it is tuned for three different cooling schemes. GRSA uses GR to dynamically decrease the SA temperature and a Markov Model to tune its parameters. Two SA tuned versions are compared in this paper: GRSA and a classical SA. Experimentation shows that the former is much more efficient than the latter. © Springer Nature

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Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding

2019 , García‐Jacas, César R. , Marrero‐Ponce, Yovani , Brizuela, Carlos A. , Suárez-Lezcano, José , Martínez Ríos, Félix Orlando

A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom-groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug-like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant-derived natural compounds exhibiting anti-cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non-fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non-truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small-to-medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.

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A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions

2021 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso

The Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used succesfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm. © 2020 The Authors. Published by Elsevier B.V.

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New metrics to modify BGP routes based on SDN

2019 , Elguea, Lorenzo M. , Martínez Ríos, Félix Orlando

Through Software Defined Network, routes obtained through Border Gateway Protocol can be modified to improve latency or select a shorter path. With the same tool that perform the above actions, you can modify routes, for example, to avoid autonomous systems in certain countries or some other policy that may help, for example, security. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

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A first-year design experience based on SAE Aero Design contest to support ABET learning outcomes and engineering vocation in freshmen student

2017 , Martínez Ríos, Félix Orlando

The a-k outcomes established by Accreditation Board for Engineering and Technology (ABET) for Engineering students in their self-assessment framework, should be reflected in the different subjects that taught to the students of the first two years of the various engineering programs. On the other hand, in those first semesters, the vocation of the students about the different Engineering is not very well defined. This experiment shows a proposal that links the results of ABET with an international student competition such as Society of Automotive Engineers (SAE) Aero Design, to reinforce and guide the new students in their future choice of specialization in the School of Engineering. We also show the relationship between the challenges and problems in the SAE Aero Design competition for new students and ABET's a-k outcomes. We show the results obtained with nineteen students over three years. It is important to mention that none of the students involved in this experiment comes from Aeronautical Engineering (or similar to it).

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Multi-threaded Spotted Hyena Optimizer with thread-crossing techniques

2021 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso

This paper presents a Multi-threaded version of the Spotted Hyena Optimizer algorithm with thread-crossing techniques (MT-SHO) to improve the ability of the algorithm to explore the search space. The original algorithm is inspired by the hunting behavior of the spotted hyena. Along the different sections of the work, we explain in detail how the original algorithm simulates the spotted hyena's behavior to optimize highly complex mathematical functions and how we handle the procedures and results of the multi-threaded version, with thread-crossing techniques that improve the ability to explore and exploit the search space by letting threads learn between them. We present the experiments used to determine the best value of the parameters used in the parallel version of the algorithm and to prove that our proposal obtains significantly good results we compare the results obtained by evaluating 24 benchmark functions with the results published for the original algorithm as well as other metaheuristic algorithms. © 2021 Elsevier B.V.. All rights reserved.

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A new swarm algorithm for global optimization of multimodal functions over multi-threading architecture hybridized with simulating annealing

2018 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso

This paper presents a new algorithm, PCLPSO, based on particle swarm optimization, which uses comprehensive learning particle swarm optimizer. Our algorithm executes C parallel CLPSO algorithms. We adopted as a criterion of completion a maximum value of evaluations of the objective function. During the execution of the CLPSO algorithms, when a certain evaluation value of the functions is reached, the best k are selected, and different initialization criteria are applied to continue the execution of the CLPSO algorithms: restarting the worst ones for the best solution or restores the worst ones to a random solution. For this restart, we use the Boltzmann criterion in a similar way as Simulating Annealing (SA) does. In this work, the experimental results obtained for the search of the minimum of 16 multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Brannin, Schwefel, and others. Our algorithm proved to be more efficient than the traditional CLPSO in its experimental results, and the nonparametric Wilcoxon test confirmed this.

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A New method to optimize BGP routes using SDN and reducing latency

2018 , Elguea, Lorenzo M. , Martínez Ríos, Félix Orlando

Latency on Internet mainly depends on the distance that packets travel in WAN networks1. This latency can be reduced if the hops between autonomous systems are reduced. This is the main function of the BGP protocol that is used by default in all the ISPs, but the lack of announcement of some network segments causes some routes to increase. This paper proposes a simple method to detect the routes that can be optimized and also a method using SDN to correct them. The first step is to determine which routes can be optimized, that is, those that are sent to a neighbor when they travel the greater distance, although this can not be determined by the router since it does not analyze all the routes that BGP receives in context. The second step is to add the new routes to the router, also by BGP, so that the router uses them. © 2018 The Authors. Published by Elsevier Ltd.

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New Particle Swarm Optimizer Algorithm with Chaotic Maps for Combinatorial Global Optimization Problems. An Application to the Deconvolution of Mössbauer Spectra

2024-01-01 , Martínez Ríos, Félix Orlando , Jiménez-López, Omar , Alvarez Guillen, Luis Alejandro

In this chapter, we present a novel method for addressing global optimization problems inspired by evolutionary algorithms found in nature. We integrate the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithm with random value generation based on chaotic maps. The resulting algorithm is applied to the computationally complex task of deconvoluting Mossbauer spectra. We implement ten chaotic maps to generate random values and compare their performance with traditional random number generators. Through experiments, we demonstrate that the developed algorithm excels in exploring the search space and exhibits fast intensification in finding the global minimum. In addition, we perform a comprehensive review of existing solutions to the Mossbauer spectrum deconvolution problem, highlighting the scarce availability of developments in this area. We also present a user-friendly program designed with an intuitive interface to facilitate the deconvolution process by Spector Mossbauer. This program will be freely distributed without operational restrictions. Experimental validation is performed on Mossbauer spectra generated using the developed program and those obtained by experimental means, affirming the efficiency of the new algorithm conceived. ©Springer.

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A new approach of the rain-fall optimization algorithm using parallelization

2020 , Guerrero-Valadez, Juan Manuel , Martínez Ríos, Félix Orlando

This chapter introduces a new implementation of the Rain-Fall Optimization Algorithm (RFO) proposed by Kaboli, Sevbaraj, and Rahim in “Rain-Fall Optimization Algorithm. A Population-Based Algorithm for Solving Constrained Optimization Problems” by Kaboli et al. (J Comput Sci 19:31–42, 2017). RFO is a nature-inspired algorithm, which is based on the behavior of the water drops produced by a rainfall going down through a mountain to find the minimum values of specific functions. The algorithm was tested on four multidimensional benchmark functions: Ackley, Griewank, Rosenbrock, and Sphere functions. It was also tested in a four-dimensional function, the Kowalik function. The first step was to match the results of the rewritten algorithm with the results obtained by the original authors. Then the algorithm had to be modified to make some efficiency improvements and to get better results. The main modifications were a new equation to modify the step size for a function called explosion process and a parallel execution of the algorithm with two different restarting techniques: restart to the best and genetic restart to the best. © Springer Nature Switzerland AG 2020.

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