Balancing exploration and exploitation in social spider optimisation using logistic chaotic map and opposition-based learning with an application to data clustering
by Ravichandran Thalamala; B. Janet; A.V. Reddy
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 17, No. 2, 2022

Abstract: Chaotic maps can be used to generate random numbers systematically. Opposition-based learning improves global searching capability of nature inspired algorithms and thereby improves the exploration. Social spider optimisation (SSO) has been getting the popularity in research community because of its applicability in a wide range of applications. The chance of getting global optimum in SSO can be improved by maintaining a balance between exploration and exploitation. In this paper, we propose a new algorithm namely logistic chaotic map and opposition-based learning SSO for data clustering (LOSSODC) that maintains a good balance between exploration and exploitation in the entire search process using logistic chaotic map and opposition-based learning for solving data clustering problem. We compare it with other nature inspired clustering algorithms and find that it gives better clustering results with respect to both low dimensional and high dimensional datasets.

Online publication date: Wed, 24-Aug-2022

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