Abstract
Multi-Agent System (MAS) appears to be an efficient, low cost, flexible, and reliable form of system, these features turns the MAS as a perfect solution for resolving complicated jobs. Personalisation is defined as the process of addressing the learner-specific techniques and their intentions or ideologies for assisting and promoting the process of an individual’s learning success. The process of both modelling and estimating the above mentioned tasks in the internet is now turning out to be a tedious task due to the continuous growth in their sizes. Here, a decentralized technique based on a multi agent optimized clustering process has been found to work well for large data sets. Genetic Algorithms (GAs) are observed as the stochastic global optimization techniques that are meant for solving the optimization problems. The Firefly algorithm (FA) is the most efficient algorithms adopted for performing the global optimization tasks in complicated search spaces. Another type of population-oriented algorithm is the Differential Evolution (DE) algorithm. In this research article a novel combination of DE and the Firefly global optimization algorithms considered as the Hybrid Firefly Algorithm Differential Evolution (HFADE) for performing the clustering tasks in an efficient manner. The effectiveness of the HFADE was experimented with benchmark functions, the achieved results shows the Hybrid algorithm well suitable for the Learning Optimisation Problems.
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23 May 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-03953-3
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-03953-3"
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Anuradha, M., Ganesan, V., Oliver, S. et al. RETRACTED ARTICLE: Hybrid firefly with differential evolution algorithm for multi agent system using clustering based personalization. J Ambient Intell Human Comput 12, 5797–5806 (2021). https://doi.org/10.1007/s12652-020-02120-w
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DOI: https://doi.org/10.1007/s12652-020-02120-w