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ew 20(29): e3

Research Article

Rain-Fall Optimization Algorithm with new parallel implementations

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  • @ARTICLE{10.4108/eai.13-7-2018.163981,
        author={Juan Manuel Guerrero-Valadez and Felix Mart\^{\i}nez-Rios},
        title={Rain-Fall Optimization Algorithm with new parallel implementations},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={7},
        number={29},
        publisher={EAI},
        journal_a={EW},
        year={2020},
        month={4},
        keywords={Optimization, Metaheuristics, Rainfall Optimization Algorithm, Multithreading, Simulated Annealing, Genetic Algorithm, Nature-inspired},
        doi={10.4108/eai.13-7-2018.163981}
    }
    
  • Juan Manuel Guerrero-Valadez
    Felix Martínez-Rios
    Year: 2020
    Rain-Fall Optimization Algorithm with new parallel implementations
    EW
    EAI
    DOI: 10.4108/eai.13-7-2018.163981
Juan Manuel Guerrero-Valadez1,*, Felix Martínez-Rios1
  • 1: Universidad Panamericana, Facultad de Ingeniería, Augusto Rodin 498, Ciudad de México, 03920, México
*Contact email: juanmanuel.guerrerovaladez@up.edu.mx

Abstract

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.

Keywords
Optimization, Metaheuristics, Rainfall Optimization Algorithm, Multithreading, Simulated Annealing, Genetic Algorithm, Nature-inspired
Received
2020-02-29
Accepted
2020-04-06
Published
2020-04-15
Publisher
EAI
http://dx.doi.org/10.4108/eai.13-7-2018.163981

Copyright © 2020 Juan Manuel Guerrero-Valadez et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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