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Random-opposition-based Learning for Computational Intelligence

Springer Verlag, (2020), 111-120
DOI: 10.1007/978-981-13-7166-0_11
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Abstract

In this paper, random-opposition-based learning (ROBL) is proposed. ROBL is a generalized version of opposition-based learning (OBL). ROBL introduces randomness in OBL. ROBL is applied for some metaheuristics and artificial neural network. The examples are provided with preliminary results.

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Bairathi, D., & Gopalani, D. (2020). Random-opposition-based Learning for Computational Intelligence. In Advances in Intelligent Systems and Computing (Vol. 933, pp. 111–120). Springer Verlag. https://doi.org/10.1007/978-981-13-7166-0_11

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