Abstract
Evolutionary algorithm exist as a subclass of artificial intelligence that requires constant optimization. It is an area of immense interest to several researchers. Different biological behaviors has formed the base for implementation of various algorithms like firefly, genetic, bee colony particle swarm optimization. Flower Pollination Algorithm (FPA) is the most recent work in this field, which use the flower pollination technique. Differential Evolution (DE) is a basic and powerful computation showing a strong global optimization. This article gives a brief about FPA and DE. Subsequently, a hybrid algorithm named as ssFPA/DE using the search strategy of flower pollination algorithm in differential evolution is applied in the field of clustering for checking the efficiency of the algorithm.
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Ramadas, M., Abraham, A., Kumar, S. (2017). Using Data Clustering on ssFPA/DE- a Search Strategy Flower Pollination Algorithm with Differential Evolution. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_53
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DOI: https://doi.org/10.1007/978-3-319-52941-7_53
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