Abstract
A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by 25.8% and data throughput by 26.86%. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.
Highlights
As a result of rapid advancements in the field of micro-electro-mechanical systems (MEMS), small sensor nodes have become inexpensive and self-sufficient [1]
A variety of network metrics are used to assess the performance of the various genetic algorithm (GA) selection methods
The Network life time (NLT) is divided into two parts: the stability period or network reliability, which lasts from the start of network operation until the death of the first node (FND), and the instability period, which lasts from the death of the first node to the death of the last node (LND)
Summary
As a result of rapid advancements in the field of micro-electro-mechanical systems (MEMS), small sensor nodes have become inexpensive and self-sufficient [1]. WSNs use meta-heuristics algorithms, which are known as nature-inspired algorithms, or intelligent optimization algorithms, to balance energy consumption among sensor nodes while reducing overall energy consumption [14,15]. This trade-off has proven to be successful. The genetic algorithm (GA) is a meta-heuristic, bio-inspired and evolutionary technique, which is widely used in WSNs to solve fundamental problems, such as sensor node localization, energy efficient clustering, data aggregation and optimal coverage [18]. With GA, the number of clusters in the network can be optimized, and sensor node energy consumption can be balanced, allowing the WSN to operate for longer time periods.
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