An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm

Shuming Sun; Yijun Chen; Ligang Dong; Shuming Sun; Yijun Chen; Ligang Dong

doi:10.3934/mbe.2024124

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 2787-2812. doi: 10.3934/mbe.2024124

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Research article

An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm

School of Information and Electronic Engineering (Sussex Artificial Intelligence Institute), Zhejiang Gongshang University, Hangzhou 310018, China

Academic Editor: Yang Kuang

Received: 14 October 2023 Revised: 21 December 2023 Accepted: 02 January 2024 Published: 24 January 2024

In response to the problem of coverage redundancy and coverage holes caused by the random deployment of nodes in wireless sensor networks (WSN), a WSN coverage optimization method called GARWOA is proposed, which combines the genetic algorithm (GA) and reinforced whale optimization algorithm (RWOA) to balance global search and local development performance. First, the population is initialized using sine map and piecewise linear chaotic map (SPM) to distribute it more evenly in the search space. Secondly, a non-linear improvement is made to the linear control factor 'a' in the whale optimization algorithm (WOA) to enhance the efficiency of algorithm exploration and development. Finally, a Levy flight mechanism is introduced to improve the algorithm's tendency to fall into local optima and premature convergence phenomena. Simulation experiments indicate that among the 10 standard test functions, GARWOA outperforms other algorithms with better optimization ability. In three coverage experiments, the coverage ratio of GARWOA is 95.73, 98.15, and 99.34%, which is 3.27, 2.32 and 0.87% higher than mutant grey wolf optimizer (MuGWO), respectively.
- wireless sensor network,
- coverage optimization,
- energy saving,
- whale optimization algorithm,
- genetic algorithm
Citation: Shuming Sun, Yijun Chen, Ligang Dong. An optimization method for wireless sensor networks coverage based on genetic algorithm and reinforced whale algorithm[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2787-2812. doi: 10.3934/mbe.2024124

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Abstract

In response to the problem of coverage redundancy and coverage holes caused by the random deployment of nodes in wireless sensor networks (WSN), a WSN coverage optimization method called GARWOA is proposed, which combines the genetic algorithm (GA) and reinforced whale optimization algorithm (RWOA) to balance global search and local development performance. First, the population is initialized using sine map and piecewise linear chaotic map (SPM) to distribute it more evenly in the search space. Secondly, a non-linear improvement is made to the linear control factor 'a' in the whale optimization algorithm (WOA) to enhance the efficiency of algorithm exploration and development. Finally, a Levy flight mechanism is introduced to improve the algorithm's tendency to fall into local optima and premature convergence phenomena. Simulation experiments indicate that among the 10 standard test functions, GARWOA outperforms other algorithms with better optimization ability. In three coverage experiments, the coverage ratio of GARWOA is 95.73, 98.15, and 99.34%, which is 3.27, 2.32 and 0.87% higher than mutant grey wolf optimizer (MuGWO), respectively.

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