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A neighborhood search based cat swarm optimization algorithm for clustering problems

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Abstract

Clustering is an unsupervised technique that groups the similar data objects into a single subset using a distance function. It is also used to find the optimal set of clusters in a given dataset and each cluster consists of homogenous data objects. In present work, an algorithm based on cat swarm optimization (CSO) is adopted for finding the optimal set of cluster centers for allocating the data objects. Further, some improvements are also incorporated in CSO algorithm for improving clustering performance. These modifications are described as an improved solution search equation to improve convergence rate and an accelerated velocity equation for balancing exploration and exploitation processes of CSO algorithm. Moreover, a neighborhood-based search strategy is introduced to handle local optima problem. The performance of proposed algorithm is tested on eight real-life datasets and compared with well-known clustering algorithms. The simulation results showed that proposed algorithm provides quality results in comparison to existing clustering algorithms.

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Abbreviations

ABC:

Artificial bee colony

ACO:

Ant colony optimization

BATC:

Bat algorithm based clustering

CABC:

Cooperative artificial bee colony

CDC:

Count to dimensions

CPSO:

Cooperative particle swarm optimization

CS:

Cuckoo search

CSO:

Cat swarm optimization

DE:

Differential evolution

FPAC:

Flower pollination algorithm based clustering

GA:

Genetic algorithm

GQCS:

Genetic quantum cuckoo search

GWA:

Grey wolf algorithm

HABC:

Hybrid artificial bee colony

HCSDE:

Hybrid cuckoo search and differential evolution

HS:

Harmony search

KCPSO:

K-means chaotic particle swarm optimization

KFCM:

Kernel based fuzzy C-means

KHM:

K-harmonic means

KICS:

K-means and improved cuckoo search

MO:

Magnetic optimization

M-TLBO:

Modified-teaching learning based optimization

PSO:

Particle swarm optimization

QPSO:

Quantum-behaved particle swarm optimization

R:

Rejected

SA:

Simulated annealing

SMP:

Seeking memory pool

SRD:

Seeking range of selected dimension

TLBO:

Teaching learning based optimization

TS:

Tabu search

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Authors and Affiliations

  1. Department of Computer Science and Engineering, JUIT, Waknaghat, Himachal Pradesh, India

    Hakam Singh & Yugal Kumar

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  1. Hakam Singh
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  2. Yugal Kumar
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Correspondence to Yugal Kumar.

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Singh, H., Kumar, Y. A neighborhood search based cat swarm optimization algorithm for clustering problems. Evol. Intel. 13, 593–609 (2020). https://doi.org/10.1007/s12065-020-00373-0

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