Browsing by Author "Cisternas Caneo, Felipe"

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    A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (BS-SCA)
    (Springer Science and Business Media Deutschland GmbH, 2022) Becerra-Rozas, Marcelo; Lemus Romani, José Isaac; Crawford, Broderick; Soto, Ricardo; Cisternas Caneo, Felipe; Trujillo Embry, Andrés; Arnao Molina, Máximo; Tapia, Diego; Castillo, Mauricio; Rubio, José Miguel
    This paper proposes a novel learnheuristic called Binary SARSA - Sine Cosine Algorithm (BS-SCA) for solving combinatorial problems. The BS-SCA is a binary version of Sine Cosine Algorithm (SCA) using SARSA to select a binarization operator. This operator is required due SCA was created to work in continuous domains. The performance of BS-SCA is benchmarked with a Q-learning version of the learnheuristic. The problem tested was the Set Covering Problem and the results show the superiority of our proposal.
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    Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review
    (2023) Becerra-Rozas, Marcelo; Lemus Romani, José Isaac; Cisternas Caneo, Felipe; Crawford, Broderick; Soto, Ricardo; Astorga, Gino; Castro, Carlos; García, José
    For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism’s impact on the metaheuristic algorithms’ performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances.
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    Reinforcement Learning Based Whale Optimizer
    (Springer International, 2021) Becerra Rozas, Marcelo; Lemus Romani, José Isaac; Crawford, Broderick; Soto, Ricardo; Cisternas Caneo, Felipe; Embry, Andres Trujillo; Molina, Maximo Arnao; Tapia, Diego; Castillo, Mauricio; Misra, Sanjay; Rubio, Jose Miguel
    This work proposes a Reinforcement Learning based optimizer integrating SARSA and Whale Optimization Algorithm. SARSA determines the binarization operator required during the metaheuristic process. The hybrid instance is applied to solve benchmarks of the Set Covering Problem and it is compared with a Q-learning version, showing good results in terms of fitness, specifically, SARSA beats its Q-Learning version in 44 out of 45 instances evaluated. It is worth mentioning that the only instance where it does not win is a tie. Finally, thanks to graphs presented in our results analysis we can observe that not only does it obtain good results, it also obtains a correct exploration and exploitation balance as presented in the referenced literature.