Martínez Ríos, Félix Orlando
Thumbnail Image
Preferred name
Martínez Ríos, Félix Orlando
Official Name
Martínez Ríos, Félix Orlando
Alternative Name
fmartin
Main Affiliation
ORCID
Scopus Author ID
24339038500
Researcher ID
N-7502-2018

Research Output
Now showing 1 - 10 of 34
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

μ𝜃-EGF: A New Multi-Thread and Nature-Inspired Algorithm for the Packing Problem

2020 , Martínez Ríos, Félix Orlando , Marmolejo Saucedo, José Antonio , García-Jacas, César R. , Murillo-Suarez, Alfonso

In this paper, the authors present a new algorithm efficient solution to the packing problem in two dimensions. The authors propose a new heuristic using the value of the electromagnetic field to determine the best position to place a circular object in a configuration of other circular objects previously packed. Also, this algorithm simulates two processes to compact objects already placed, inspired by gravitational forces, to minimize the empty space in the container and maximizing the number of objects in the container. To determine the efficacy of this algorithm, the authors carried out experiments with twenty-four instances. Parallel computing can contribute to making decision processes such as optimization and prediction more agile and faster. Real-time decision making involves the use of solution methodologies and algorithms. For this reason the present manuscript shows an alternative for the solution of a classic industry problem that must be solved quickly. Packaging optimization can help reduce waste of container material. The material used to transport the products can reduce its environmental impact due to an efficient packaging process. Light-weighting can also be accomplished by reducing the amount of packaging material used. © Springer Nature

View
No Thumbnail Available
Publication

An efficient method to compare latencies in order to obtain the best route for SDN

2017 , Elguea, Lorenzo M. , Martínez Ríos, Félix Orlando

Comparing two or more routes on Internet is difficult owing to the variability of the measurements resulting from the different routes or use conditions. With current tools such as SDN[1], it is important to determine with certainty which the best route between a user and an internet service. This will be achieved with fast measurements which do not affect the operation of the network. With trends such as IoT, the best routes can be identified based on latency and not just on the jumps between autonomous systems, fact that optimizes data traffic in a specific way whether it is IPv4 or IPv6. As time elapses, it becomes more important to have a perfect setting for the LAN, which means optimal DNS, LDAP Servers appropriate number, etc. Thats why we propose a precise method that contemplates every possible variation of data, thus making a comparison by means of the use of confidence limits.

View
No Thumbnail Available
Publication

A new heuristic with a multi-threaded implementation of a modified Firefly Algorithm

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

In this article, we present a modified version of the Firefly Algorithm implemented in a multi-threaded model to improve the results obtained by the original algorithm significantly. This multi-threaded algorithm allows the threads to obtain different results by the independent execution of the heuristic method in each of them, although for keeping all the threads with significant executions, the algorithm performs some crossover techniques, explained in detail in this article, for the threads to learn between them while maintaining its independence. For testing the new algorithm, we use the six benchmark functions used in the literature for testing the original Firefly Algorithm, and to prove that the improved results are significant, we perform the Wilcoxon test to the results obtained. The results obtained with this new heuristic proved to be significantly better while taking advantage of today's commercial processors. © 2020 Alfonso Murillo-Suarez et al., licensed to EAI.

View
No Thumbnail Available
Publication

Handcrafted versus non-handcrafted (self-supervised) features for the classification of antimicrobial peptides: complementary or redundant?

2022 , García-Jacas, César R. , García-González, Luis A. , Martínez Ríos, Félix Orlando , Tapia-Contreras, Issac P. , Brizuela, Carlos A.

Antimicrobial peptides (AMPs) have received a great deal of attention given their potential to become a plausible option to fight multidrug resistant bacteria as well as other pathogens. Quantitative sequence-activity models (QSAMs) have been helpful to discover new AMPs because they allow to explore a large universe of peptide sequences and help reduce the number of wet lab experiments. A main aspect in the building of QSAMs based on shallow learning is to determine an optimal set of protein descriptors (features) required to discriminate between sequences with different antimicrobial activities. These features are generally handcrafted from peptide sequence datasets that are labeled with specific antimicrobial activities. However, recent developments have shown that unsupervised approaches can be used to determine features that outperform human-engineered (handcrafted) features. Thus, knowing which of these two approaches contribute to a better classification of AMPs, it is a fundamental question in order to design more accurate models. Here, we present a systematic and rigorous study to compare both types of features. Experimental outcomes show that non-handcrafted features lead to achieve better performances than handcrafted features. However, the experiments also prove that an improvement in performance is achieved when both types of features are merged. A relevance analysis reveals that nonhandcrafted features have higher information content than handcrafted features, while an interaction-based importance analysis reveals that handcrafted features are more important. These findings suggest that there is complementarity between both types of features. Comparisons regarding state-of-the-art deep models show that shallow models yield better performances both when fed with non-handcrafted features alone and when fed with non-handcrafted and handcrafted features together.

View
No Thumbnail Available
Publication

Innovative Alignment-Based Method for Antiviral Peptide Prediction

2024 , Daniela de Llano García , Marrero Ponce, Yovani , Guillermin Agüero-Chapin , Francesc J. Ferri , Agostinho Antunes , Martínez Ríos, Félix Orlando , Hortensia Rodríguez

Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models’ robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew’s correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.

View
No Thumbnail Available
Publication

A new hybridized algorithm based on Population-Based Simulated Annealing with an experimental study of phase transition in 3-SAT

2017 , Martínez Ríos, Félix Orlando

This paper is about experiments for satisfiability problem using a new algorithm (GR-MM-PBSA) that improves the algorithm Population-Based Simulated Annealing (PBSA). GR-MM-PBSA runs in a parallel way Simulated Annealing (SA) and Threshold Annealing (TA) algorithms with a Golden Ratio space search strategy and Markovian Model to select initial and final temperature. In this paper we execute differents hybridized Simulated Annealing (or Threshold Accepting) algorithms and compares the efficiency of these, using a metric based on transition phase effect. Simulated Annealing Algorithms (SAA) theoretically can reach the optimum if the control parameters and cooling scheme are chosen correctly. All algorithms are compared with a metric based on transition phase obtained for 3-SAT instances. This paper shows the results of SAA hybridizations are more efficient than the original algorithm, without increasing their computational complexity. We also show the experimental data about runs with 820 3-SAT instances with ratio clauses-variables between 2.0 to 6.0.

View
No Thumbnail Available
Publication

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.

View
No Thumbnail Available
Publication

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

2022 , Romero, Maylin , Marrero-Ponce, Yovani , Rodríguez, Hortensia , Agüero-Chapin, Guillermin , Antunes, Agostinho , Aguilera-Mendoza, Longendri , Martínez Ríos, Félix Orlando

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

View