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Language: search.filters.language.EnglishSubject: search.filters.subject.Nature-inspired algorithm

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    A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions
    (2021)
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    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.
    Scopus© Citations 2  26  1
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    MTGWA: A Multithreaded Gray Wolf Algorithm with Strategies Based on Simulated Annealing and Genetic Algorithms
    (2021)
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    Murillo-Suarez, Alfonso
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    García-Jacas, Cesar Raúl
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    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
    Scopus© Citations 1  30  2
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    Packing algorithm inspired by gravitational and electromagnetic effects
    (2019)
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    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
    Scopus© Citations 2  9  2