Abstract
Elephant Herding Optimization (EHO) is a nature-inspired algorithm reported by Wang et al in 2015. The algorithm has a mixed nature of swarm intelligence (behaviour of elephant living in groups) and evolutionary algorithm (reproduction to create baby elephant). It has both exploitation (clan updating operator) and exploration (separating operator) capability to make it a potential algorithm for optimization. In this chapter, the EHO has been suitably formulated to perform clustering task by minimizing intra-cluster distance as cost function. Simulation is demonstrated on cluster analysis of three synthetic and six benchmark datasets. Comparative analysis with RCGA, PSO, and K-means algorithm demonstrate superior percentage accuracy of EHO in the form of box plots. It is also observed that computational time of EHO is higher than K-means but lower than PSO and RCGA.
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Jaiprakash, K.P., Nanda, S.J. (2019). Elephant Herding Algorithm for Clustering. In: Kalita, J., Balas, V., Borah, S., Pradhan, R. (eds) Recent Developments in Machine Learning and Data Analytics. Advances in Intelligent Systems and Computing, vol 740. Springer, Singapore. https://doi.org/10.1007/978-981-13-1280-9_30
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DOI: https://doi.org/10.1007/978-981-13-1280-9_30
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