ew 18: 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: Online First},
        keywords={Optimization, Metaheuristics, Rainfall Optimization Algorithm, Multithreading, Simulated Annealing, Genetic Algorithm, Nature-inspired},
  • Juan Manuel Guerrero-Valadez
    Felix Martínez-Rios
    Year: 2020
    Rain-Fall Optimization Algorithm with new parallel implementations
    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


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.