2I-CSO: A Novel Intelligent and Interoperable Cat Swarm Optimizer Approach to Optimal Cluster Head Selection and Self-Termination Search

Abstract

Wireless sensor networks (WSNs), fundamental building block of IoT, are subject to several constraints because of the finite non-rechargeable energy resources available in the nodes. The selection of Cluster Head (CH) plays a critical role in determining the energy balance in a network. Conventional methods such as the LEACH algorithm choose CH randomly with a probability mechanism that might lead to choosing weak nodes as CHs and thereby fail prematurely. The biologically -inspired optimization methods, such as PSO and CSO, help to enhance CH selection using a global approach. However, these methods suffer from the following three major shortcomings: 1) random switching between the exploration and exploitation stages, 2) lack of intelligence during the formation of clusters, and 3) growing exponentially complex search space of CHs.This study proposes an Intelligent and Interoperable Cat Swarm Optimizer (2I-CSO), a protocol designed to address these limitations simultaneously. 2I-CSO also introduces an interoperable configuration mechanism based on LEACH’s hierarchical architecture, where the Base Station maintains a centralized energy configuration table shared with Cluster Heads and member nodes, ensuring network-wide parameter consistency and enabling the intelligent stopping mechanism. Experiments conducted on five well-known TSPLIB test cases and WSN simulations demonstrate that 2I-CSO outperforms individual metaheuristics. Simulation results on a custom web-based platform further show that 2I-CSO achieves faster convergence, lower computational cost, and competitive network lifetime compared to standard CSO and the Emperor Penguin Optimizer (EPO). To the best of our knowledge, the proposed intelligent stopping condition is the first introduced for bio-inspired WSN clustering protocols.