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Swarm based automatic clustering using nature inspired Emperor Penguins Colony algorithm

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Abstract

Nature acts as a source of concepts, mechanisms, and principles for designing artificial computing systems to deal with complex computational problems. Most heuristic and metaheuristic algorithms are taken from the behavior of biological systems or physical systems in nature. Clustering is the process of grouping a set of data and putting it in a class of similar examples. Since the clustering problem is an NP-hard problem, using metaheuristics can be an appropriate tool to deal with these issues. Indeed, clustering is a special case of an optimization problem. In classic clustering, knowing the number of clusters is required before clustering. This paper presents an algorithm that requires no prior knowledge to classify the data. In this paper, we proposed a swarm-based Emperor Penguins Colony (EPC) algorithm to solve both classic and automatic clustering problems. The proposed approach is compared with six state-of-the-art, popular, and improved nature-inspired algorithms, a partitioning-based heuristic algorithm, and a hierarchical clustering method on ten real-world datasets. The results show that classic and automatic clustering using the EPC algorithm has better performance in comparison with other competing algorithms.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Karaj Branch, Karaj, Iran

    Sasan Harifi, Madjid Khalilian & Javad Mohammadzadeh

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  1. Sasan Harifi
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  2. Madjid Khalilian
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  3. Javad Mohammadzadeh
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Correspondence to Madjid Khalilian.

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Harifi, S., Khalilian, M. & Mohammadzadeh, J. Swarm based automatic clustering using nature inspired Emperor Penguins Colony algorithm. Evolving Systems 14, 1083–1099 (2023). https://doi.org/10.1007/s12530-023-09507-y

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