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首页> 外文期刊>IFAC PapersOnLine >Wireless Sensor Network Lifespan Optimization with Simple, Rotated, Order and Modified Partially Matched Crossover Genetic Algorithms
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Wireless Sensor Network Lifespan Optimization with Simple, Rotated, Order and Modified Partially Matched Crossover Genetic Algorithms

机译：简单，旋转，有序和修正的部分匹配交叉遗传算法的无线传感器网络寿命优化

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Abstract: This paper proposes a genetic algorithm (GA) with a benchmarking study for optimizing the wireless sensor network (WSN) lifespan. Four crossover operators combined with four mutation operators were developed to enhance the GA efficiency, and thus the performance of the lifespan optimization algorithm. The traditional one-point crossover operator referred as the "simple crossover", is used to evaluate the modified "partially matched" and the "order" crossovers. Also, a new crossover operator referred as "rotated" crossover is also proposed and evaluated. Different combinations of these crossover operators with the one-point and two-points deterministic and random mutations are used to optimize the WSNs lifespan. The algorithms were coded in C programming language and applied to different instances of WSNs initial configurations. The optimization software tool developed based on the combinatorial operators allows selecting the best solution among 16 through a smart decision making. For illustration, among all the investigated algorithms, the modified partially matched crossover associated with the random two-points mutation has shown the best performances on the studied instance, due to its capability to reach closer to the optimal solution.
机译：摘要：本文提出了一种遗传算法（GA），并进行了基准研究，以优化无线传感器网络（WSN）的使用寿命。开发了四个交叉算子和四个变异算子，以提高GA效率，从而提高了寿命优化算法的性能。传统的单点交叉算子称为“简单交叉”，用于评估修改后的“部分匹配”和“有序”交叉。另外，还提出并评估了一种新的交叉算子，称为“旋转”交叉。这些交叉算子与一点和两点确定性和随机突变的不同组合用于优化WSN的寿命。该算法以C编程语言编码，并应用于WSN初始配置的不同实例。基于组合运算符开发的优化软件工具允许通过智能决策在16种之中选择最佳解决方案。为了说明，在所有研究的算法中，与随机两点突变相关联的修改后的部分匹配交叉因其能够更接近最优解而具有在研究实例上的最佳性能。

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