Multi-strategy Improved Northern Goshawk Optimization for WSN Coverage Enhancement

TL;DR

This paper introduces a multi-strategy enhanced Northern Goshawk Optimization algorithm to improve Wireless Sensor Network coverage, employing chaotic mapping and evolutionary strategies to achieve superior coverage and connectivity results.

Contribution

It presents a novel multi-strategy NGO algorithm with chaotic mapping and evolutionary dynamics for WSN coverage optimization, outperforming existing methods.

Findings

Significant coverage improvement over benchmarks

Enhanced node connectivity in WSNs

Effective avoidance of local optima

Abstract

To enhance the coverage rate of Wireless Sensor Networks (WSNs), this paper proposes an advanced optimization strategy based on a multi-strategy integrated Northern Goshawk Optimization (NGO) algorithm. Specifically, multivariate chaotic mapping is first employed to improve the randomness and uniformity of the initial population. To further bolster population diversity and prevent the algorithm from stagnating in local optima, a bidirectional population evolutionary dynamics strategy is incorporated following the pursuit-and-evasion phase, thereby facilitating the attainment of the global optimal solution. Extensive simulations were conducted to evaluate the performance of the proposed multi-strategy NGO in WSN coverage. Experimental results demonstrate that the proposed algorithm significantly outperforms existing benchmarks in terms of both coverage enhancement and node connectivity.