Martínez Ríos, Félix Orlando
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Martínez Ríos, Félix Orlando
Official Name
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 - 8 of 8
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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.

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A new heuristic algorithm to solve Circle Packing problem inspired by nanoscale electromagnetic fields and gravitational effects

2018 , Martínez Ríos, Félix Orlando , Marmolejo Saucedo, José Antonio , Murillo-Suarez, Alfonso

In this paper, we present a new algorithm for the fast and efficient solution of the Packing problem in two dimensions. The packing problem consists in finding the best arrangement of objects (many geometrical forms) in a specific space called container.This new algorithm is inspired by the observations of nanometric scale electromagnetic fields. We use the electromagnetic theory of the electric field to calculate the best position to place a circular object in a configuration of other circular objects previously packing. Also, in this new algorithm we simulate two processes called »gravity» and »shaken» that compact the distribution of the objects placed in the container and allow to minimize the unoccupied space. © 2018 IEEE.

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An Alternative Method for the Optimum Dynamic Balancing of the Four-Bar Mechanism

2014 , Acevedo, Mario , Haro-Sandoval, Eduardo , Martínez Ríos, Félix Orlando

This article presents the optimum dynamic balancing of the four-bar mechanism, in particular the crank-rocker, by the addition of counterweights. This is done by imposing as little restrictive as possible constraints on the counterweights parameters. First the general analytical equations of motion of the crank-rocker four-bar mechanism are obtained, using natural coordinates. This model allows expressing the dynamic equations of the mechanism just in terms of the mass, as opposed to the need of using also the moment of inertia, and the coordinates of the center of gravity of the counterweights, that are used as optimization variables. This implies that no particular counterweight shape is assumed in advance. The only constraints imposed on these optimization variables are that masses must be non-negative. As a novelty, the most influencing variables in the optimization are identified using a global sensitivity analysis based on polynomial chaos. This allows to impose different constraints an also to reduce the total number of optimization variables without affecting the global results. The results obtained are validated by simulations, and compared to those expressed in representative papers obtained by other authors. © Springer Nature

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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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Packing algorithm inspired by gravitational and electromagnetic effects

2019 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso

This paper introduces a faster and more efficient algorithm for solving a two-dimension packing problem. This common optimization problem takes a set of geometrical objects and tries to find the best form of packing them in a space with specific characteristics, called container. The visualization of nanoscale electromagnetic fields was the inspiration for this new algorithm, using the electromagnetic field between the previously placed objects, this paper explains how to determine the best positions for to place the remaining ones. Two gravitational phenomena are also simulated to achieve better results: shaken and gravity. They help to compact the objects to reduce the occupied space. This paper shows the executions of the packing algorithm for four types of containers: rectangles, squares, triangles, and circles. © Springer Nature

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MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms

2021 , Martínez Ríos, Félix Orlando , Murillo-Suarez, Alfonso , García-Jacas, Cesar Raúl , Guerrero-Valadez, Juan Manuel

In this paper, we present an improvement of the Gray Wolf algorithm (GWO) based on a multi-threaded implementation of the original algorithm. The paper demonstrates how to combine the solutions obtained in each of the threads to achieve a final solution closer to the absolute minimum or even equal to it. To properly combine the solutions of each of the threads of execution, we use strategies based on simulated annealing and genetic algorithms. Also, we show the results obtained for twenty-nine functions: unimodal, multimodal, fixed dimension and composite functions. Experiments show that our proposed improves the results of the original algorithm. © Springer Nature

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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

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Rain-Fall Optimization Algorithm with new parallel implementations

2018 , Guerrero-Valadez, Juan Manuel , Martínez Ríos, Félix Orlando

Rainfall Optimization Algorithm (RFO) is a nature-inspired metaheuristic optimization algorithm. RFO mimics the movement of water drops generated during rainfall to optimize a function. The paper study new implementations for RFO to offer more reliable results. Moreover, it studies three restarting techniques that can be applied to the algorithm with multithreading. The different implementations for the RFO are benchmarked to test and verify the performance and accuracy of the solutions. The paper presents and compares the results using several multidimensional testing functions, as well as the visual behavior of the raindrops inside the benchmark functions. The results confirm that the movement of the artificial drops corresponds to the natural behavior of raindrops. The results also show the effectiveness of this behavior to minimize an optimization function and the advantages of parallel computing restarting techniques to improve the quality of the solutions.

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