A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (BS-SCA)

Becerra-Rozas, Marcelo; Lemus-Romani, José; Crawford, Broderick; Soto, Ricardo; Cisternas-Caneo, Felipe; Embry, Andrés Trujillo; Tapia, Diego; Castillo, Mauricio; Rubio, José-Miguel

doi:10.1007/978-3-030-94216-8_10

Cited by 2 publications

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References 22 publications

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“…Putting these concepts into practice, we specifically engaged 2 activities. (1) The authors in [20,21] proposed SARSA as a dynamic binarization scheme selector in the continuous metaheuristic. Motivated by the first point and seeking to answer our research question: "Does an increase of the transfer functions to be used in the binarization scheme selector positively affect the optimization process?"…”

Section: Introductionmentioning

confidence: 99%

Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

et al. 2022

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When we face real problems using computational resources, we understand that it is common to find combinatorial problems in binary domains. Moreover, we have to take into account a large number of possible candidate solutions, since these can be numerous and make it complicated for classical algorithmic techniques to address them. When this happens, in most cases, it becomes a problem due to the high resource cost they generate, so it is of utmost importance to solve these problems efficiently. To cope with this problem, we can apply other methods, such as metaheuristics. There are some metaheuristics that allow operation in discrete search spaces; however, in the case of continuous swarm intelligence metaheuristics, it is necessary to adapt them to operate in discrete domains. To perform this adaptation, it is necessary to use a binary scheme to take advantage of the original moves of the metaheuristics designed for continuous problems. In this work, we propose to hybridize the whale optimization algorithm metaheuristic with the Q-learning reinforcement learning technique, which we call (the QBWOA). By using this technique, we are able to realize an smart and fully online binarization scheme selector, the results have been statistically promising thanks to the respective tables and graphs.

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Section: Introductionmentioning

confidence: 99%

Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

et al. 2022

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Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

et al. 2022

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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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A New Learnheuristic: Binary SARSA - Sine Cosine Algorithm (BS-SCA)

Cited by 2 publications

References 22 publications

Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

Embedded Learning Approaches in the Whale Optimizer to Solve Coverage Combinatorial Problems

Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review

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