An Improved FTTA Algorithm: Intelligent Dynamic Orthogonal Football Team Training Algorithm is Applied to WSN Coverage Problem

The coverage optimization problem in Wireless Sensor Networks (WSNs) is a critical challenge for improving network sensing performance and resource utilization efficiency. However, the high-dimensional solution space and complex constraints present significant difficulties for conventional optimization algorithms. This study proposes an enhanced Football Team Training Algorithm (FTTA), termed the Intelligent Dynamic Orthogonal Football Team Training Algorithm (IDOFTTA). The algorithm incorporates three novel strategies: Schmidt orthogonal adaptive mutation, intelligent mode-switching optimization, and elite exclusion with dynamic reset mechanisms. The proposed IDOFTTA algorithm incorporates three key innovations: Schmidt orthogonal mutation enhances population diversity through orthogonalized search directions, intelligent mode-switching optimization dynamically adjusts role transition probabilities to balance global exploration and local exploitation, and an elite exclusion mechanism effectively prevents premature convergence and local optima traps. The experimental validation was conducted using the CEC2017 benchmark suite to evaluate IDOFTTA against eight state-of-the-art optimization algorithms, including Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO). The experimental results demonstrate that IDOFTTA achieved first-rank mean performance in 82% of 30dimensional test functions and 93% of 100-dimensional cases. Notably, its standard deviation and optimal values were significantly superior to those of comparative algorithms. This superiority was statistically validated through Friedman tests at a 95% confidence level. Furthermore, the practical efficacy of IDOFTTA was evaluatedthrough its application to WSN coverage optimization. Based on the Boolean sensing model, the algorithm demonstrated significant performance improvements in realworld engineering scenarios. Specifically, a substantial enhancement in coverage rate was observed and quantitatively verified.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-17
DOI
https://doi.org/10.5281/zenodo.15679244
Primary Topic
Energy Efficient Wireless Sensor Networks
Type
article
Field-Weighted Citation Impact
0.00
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article

An Improved FTTA Algorithm: Intelligent Dynamic Orthogonal Football Team Training Algorithm is Applied to WSN Coverage Problem

Zenodo (CERN European Organization for Nuclear Research)
Energy Efficient Wireless Sensor Networks
article

An Improved FTTA Algorithm: Intelligent Dynamic Orthogonal Football Team Training Algorithm is Applied to WSN Coverage Problem

article en

Abstract

The coverage optimization problem in Wireless Sensor Networks (WSNs) is a critical challenge for improving network sensing performance and resource utilization efficiency. However, the high-dimensional solution space and complex constraints present significant difficulties for conventional optimization algorithms. This study proposes an enhanced Football Team Training Algorithm (FTTA), termed the Intelligent Dynamic Orthogonal Football Team Training Algorithm (IDOFTTA). The algorithm incorporates three novel strategies: Schmidt orthogonal adaptive mutation, intelligent mode-switching optimization, and elite exclusion with dynamic reset mechanisms. The proposed IDOFTTA algorithm incorporates three key innovations: Schmidt orthogonal mutation enhances population diversity through orthogonalized search directions, intelligent mode-switching optimization dynamically adjusts role transition probabilities to balance global exploration and local exploitation, and an elite exclusion mechanism effectively prevents premature convergence and local optima traps. The experimental validation was conducted using the CEC2017 benchmark suite to evaluate IDOFTTA against eight state-of-the-art optimization algorithms, including Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Golden Jackal Optimization (GJO). The experimental results demonstrate that IDOFTTA achieved first-rank mean performance in 82% of 30dimensional test functions and 93% of 100-dimensional cases. Notably, its standard deviation and optimal values were significantly superior to those of comparative algorithms. This superiority was statistically validated through Friedman tests at a 95% confidence level. Furthermore, the practical efficacy of IDOFTTA was evaluatedthrough its application to WSN coverage optimization. Based on the Boolean sensing model, the algorithm demonstrated significant performance improvements in realworld engineering scenarios. Specifically, a substantial enhancement in coverage rate was observed and quantitatively verified.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Openalex Percentile: Top 51%
Energy Efficient Wireless Sensor Networks
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An Improved FTTA Algorithm: Intelligent Dynamic Orthogonal Football Team Training Algorithm is Applied to WSN Coverage Problem · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS