改进多目标蚁狮算法的WSNs节点部署策略

doi:10.19665/j.issn1001-2400.2022.05.006

Abstract

Abstract:

In order to balance the coverage,connectivity and number of nodes in the deployment of wireless sensor networks (WSNs),a multi-objective node deployment model with the minimum coverage and connectivity between nodes as the constraint conditions is constructed,and by using the ideology of the Pareto optimal solution set,a node deployment strategy based on the improved multi-objective ant-lion Algorithm (IMOALO) is proposed.First,the Fuch chaotic map is used to initialize the population and increase the diversity of population.At the same time,the performance of the IMOALO is improved by introducing the adaptive shrinkage boundary factor,which can overcome the shortcoming of being easily plunged into local optimal of the MOALO.Second,the time-varying strategy position disturbance is used to the ant for improving the optimization ability of the algorithm.Third,a comparison of the test function with other multi-objective algorithms shows that the improved algorithm can lead to the minimum GD and IGD values on different test functions,which verifies the effectiveness of the proposed strategy.Finally,the IMOALO is applied to the multi-objective node deployment in WSNs.Simulation results show that compared with other multi-objective algorithms,the IMOALO can effectively solve the multi-objective optimization deployment problem of the nodes in WSNs,improve the coverage and connectivity of the monitoring area,and provide more feasible solutions for decision makers.

Key words: wireless sensor networks, node deployment, multi-objective optimization, ant lion algorithm, Pareto principle

CLC Number:

TP393

ZHANG Hao, QIN Tao, XU Linghua, WANG Xiao, YANG Jing. WSNs node deployment strategy based on the improved multi-objective ant-lion algorithm[J].Journal of Xidian University, 2022, 49(5): 47-59.

Figures/Tables 16

References 28

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算法	ZDT1		ZDT2		ZDT3		ZDT4		ZDT6
算法	Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
NSGA-Ⅱ	0.068 5	0.045 2	0.195 7	0.078 1	0.046 7	0.031 3	0.127 7	0.098 5	0.032 9	0.020 4
MOPSO	0.016 0	0.016 7	0.017 9	0.016 9	0.040 2	0.045 4	0.371 2	0.214 1	0.008 8	0.003 7
MOALO	0.080 3	0.046 7	0.069 7	0.056 4	0.085 2	0.042 0	0.132 5	0.070 5	0.022 4	0.010 1
CMOALO	0.099 2	0.059 4	0.138 3	0.045 2	0.203 6	0.005 3	0.122 7	0.065 3	0.015 2	0.005 1
IMOALO	0.008 0	0.001 3	0.008 0	0.000 8	0.008 2	0.000 4	0.008 1	0.001 0	0.003 1	0.000 6

算法	ZDT1		ZDT2		ZDT3		ZDT4		ZDT6
算法	Mean	Std	Mean	Std	Mean	Std	Mean	Std	Mean	Std
NSGA-Ⅱ	0.002 7	0.001 4	0.000 5	0.000 2	0.001 9	0.002 1	0.006 5	0.005 3	0.004 2	0.002 8
MOPSO	0.000 5	0.000 5	0.002 0	0.002 3	0.000 7	0.000 5	0.003 6	0.003 4	0.010 1	0.006 1
MOALO	0.003 8	0.003 0	0.004 0	0.001 1	0.003 3	0.002 5	0.003 9	0.003 2	0.004 1	0.003 2
CMOALO	0.001 2	0.002 2	0.002 4	0.000 8	0.002 5	0.000 9	0.001 6	0.001 1	0.002 8	0.001 4
IMOALO	0.000 5	0.000 2	0.000 4	0.000 1	0.000 6	0.000 3	0.001 0	0.000 2	0.002 2	0.000 2

方案编号	目标函数1	目标函数2	方案编号	目标函数1	目标函数2	方案编号	目标函数1	目标函数2
1	0.034 8	0.019 2	11	0.327 9	0.010 5	21	0.502 7	0.005 8
2	0.035 9	0.017 9	12	0.330 3	0.010 0	22	0.511 9	0.005 7
3	0.036 9	0.017 5	13	0.332 5	0.009 8	23	0.514 9	0.005 6
4	0.037 3	0.017 2	14	0.370 8	0.009 7	24	0.515 4	0.005 6
5	0.037 9	0.016 9	15	0.372 5	0.009 6	25	0.516 9	0.005 5
6	0.131 8	0.014 3	16	0.375 7	0.009 5	26	0.521 4	0.005 5
7	0.210 4	0.013 0	17	0.441 3	0.007 4	27	0.522 2	0.005 4
8	0.258 9	0.012 2	18	0.471 6	0.006 8	28	0.526 3	0.005 3
9	0.272 7	0.012 0	19	0.480 3	0.006 5	29	0.554 7	0.004 1
10	0.327 4	0.010 6	20	0.497 3	0.006 1	30	0.555 0	0.004 0

WSNs node deployment strategy based on the improved multi-objective ant-lion algorithm

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References 28

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