Rat Swarm Optimizer for Data Clustering

Abstract

Rat Swarm Optimization (RSO) is one of the newest swarm intelligence optimization algorithms that is inspired from the behaviors of chasing and fighting of rats in nature. In this paper we will apply the RSO to one of the most challenging problems, which is data clustering. The search capability of RSO is used here to find the best clusters centers. The proposed algorithm RSO for clustering (RSOC) is tested on several benchmarks and compared to some other optimization algorithms for data clustering including some wellknown and powerful algorithms such as Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) and other recent such as the Hybridization of Krill Herd Algorithm and harmony search (H-KHA), hybrid Harris Hawks Optimization with differential evolution (H-HHO), and Multi-Verse Optimizer (MVO). Results are validated through a bunch of measures: homogeneity, completeness, v-measure, purity, and error rate. The computational results are encouraging, Where they demonstrate the effectiveness of RSOC over other techniques.

Solving the Vehicle Routing Problem with Time Windows Using Modified Rat Swarm Optimization Algorithm Based on Large Neighborhood Search

“…(2023) present a trust-aware clustering technique based on the rat swarm optimization algorithm for the secure selection of cluster heads in wireless sensor networks for intelligent transportation systems [26]. Ibrahim et al (2022) utilize the search capability of the rat swarm optimization algorithm to identify optimal cluster centers, demonstrating its effectiveness over other clustering techniques [27]. Xie et al (2022) introduce a multi-strategy modified rat swarm optimization algorithm that effectively addresses path planning for mobile robots and enhances global search capability and optimization efficiency [28].…”