An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm
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View abstract on PubMed
Summary
This summary is machine-generated.The GARWOA method enhances wireless sensor network coverage by combining genetic and reinforced whale optimization algorithms. This approach effectively addresses coverage gaps and redundancy, improving overall network performance.
Area Of Science
- Computer Science
- Network Engineering
- Optimization Algorithms
Background
- Random node deployment in wireless sensor networks (WSN) leads to coverage redundancy and holes.
- Existing optimization methods struggle to balance global search and local development effectively.
Purpose Of The Study
- To propose a novel WSN coverage optimization method, GARWOA, that integrates genetic algorithm (GA) and reinforced whale optimization algorithm (RWOA).
- To enhance the performance of the whale optimization algorithm (WOA) for improved exploration and development efficiency in WSN coverage optimization.
Main Methods
- Population initialization using sine map and piecewise linear chaotic map (SPM) for even distribution.
- Non-linear improvement of the WOA control factor 'a' to boost exploration and development.
- Incorporation of a Levy flight mechanism to mitigate local optima and premature convergence.
Main Results
- GARWOA demonstrated superior optimization ability across 10 standard test functions compared to other algorithms.
- In coverage experiments, GARWOA achieved coverage ratios of 95.73%, 98.15%, and 99.34%.
- GARWOA outperformed the mutant grey wolf optimizer (MuGWO) by 3.27%, 2.32%, and 0.87% in coverage ratio.
Conclusions
- GARWOA effectively balances global search and local development for WSN coverage optimization.
- The proposed method significantly improves coverage ratios and overcomes limitations of existing algorithms.
- GARWOA offers a robust solution for addressing coverage redundancy and holes in wireless sensor networks.
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