Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (3): 761-770.doi: 10.13229/j.cnki.jdxbgxb.20220566

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Improved sparrow search algorithm optimizes coverage in wireless sensor networks

Jin DUAN(),An-ni YAO,Zhen WANG,Lin-tao YU   

  1. School of Electronic Information Engineering，Changchun University of Science and Technology，Changchun 130012，China
  • Received:2022-07-15 Online:2024-03-01 Published:2024-04-18

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Abstract:

In response to the challenges of significant randomness and susceptibility to local optima in the sparrow search algorithm， an enhanced approach was proposed integrating multiple strategies. During the initialization phase， a good point set strategy was introduced to ensure population diversity and thorough exploration. The discoverer's position update incorporates a dynamic learning mechanism， effectively balancing global optimization and local exploration capabilities. Simultaneously， the follower's position update integrates a Lévy flight disturbance mechanism， reinforcing local escape capabilities. Finally, the proposed method was applied to solve the coverage problem of wireless sensor networks. Through a multi-objective coverage optimization function， considering coverage rate maximizing， redundancy minimization， and energy consumption equilibrium maximizing. The simulation results show that the three improvement measures significantly improve the algorithm performance, enhance the coverage quality of network nodes, and effectively improve the overall performance of the network, proving that the proposed method has good performance in practical applications.

Key words: information processing technology, sparrow search algorithm, wireless sensor network coverage, good point set initialization, dynamic learning mechanism, Lévy flight strategy

CLC Number: 

  • TP301.6

Table 1

Standard test functions"

函 数维度区间最小值
F1(x)=∑i=1nxi230［-100，100］0
F2(x)=∑i=1nxi+∏i=1nxi30［-10，10］0
F3(x)=∑i=1n∑j=1jxj230［-100，100］0
F4(x)=maxi{xi,1≤i≤n}30［-100，100］0
F5(x)=∑i=1n-xisin(xi)30［-500，500］-418.982 9n
F6(x)=∑i=1n[xi2-10cos(2πxi+10)]30［-5.12，5.12］0
F7(x)=-20exp-0.21n∑i=1nxi2-exp1ncos(2πxi)+20+e30［-32，32］0
F8(x)=14000xi2-∏i=1ncosxii+130［-600，600］0
F9(x)=1500+∑j=1251j+∑i=12(xi-aij)6-12［-65，65］1
F10(x)=∑i=111ai-x1(bi2+b1x2)bi2+b1x3+x424［-5，5］0.000 30
F11(x)=-∑i=14ciexp-∑j=13aij(xj-pij)23［0，1］-3.86
F12(x)=-ciexp-∑j=16aij(xj-pij)26［0，1］-3.32

Table 2

Comparison of the running results of each algorithm"

函数指标SSAPSOHPSOBOACSSAGGSC-SSALOSSAMSSA
F1(x)最优值3.1280×10-281.0318×10-182.5812×10-1521.1676×10-1365.5906×10-486.1795×10-2060
均值2.5686×10-271.0884×10-153.7236×10-1525.2212×10-241.9126×10-247.6514×10-1710
标准差3.8704×10-276.9952×10-107.6134×10-1532.3350×10-232.4460×10-116.4150×10-1270
F2(x)最优值3.3588×10-173.5539×10-91.1843×10-657.7504×10-177.8221×10-4600
均值7.9059×10-172.4834×10-82.0909×10-601.1937×10-112.0384×10-395.5227×10-1590
标准差5.4325×10-176.9702×10-94.9121×10-609.5953×10-93.2288×10-392.8718×10-1250
F3(x)最优值8.5183×10-145.1376×10-114.8897×10-421.3048×10-231.4358×10-421.2112×10-1530
均值3.6673×10-106.2950×10-112.3992×10-404.7707×10-217.7168×10-419.6838×10-1530
标准差8.1535×10-107.1649×10-123.0600×10-406.0171×10-211.1485×10-409.6854×10-1530
F4(x)最优值1.0952×10-242.4150×10-86.7690×10-777.5195×10-18704.0623×10-830
均值4.6230×10-243.4641×10-87.7480×10-773.7362×10-724.5282×10-633.8551×10-736.9195×10-95
标准差2.9201×10-244.5498×10-94.4423×10-781.7870×10-282.0251×10-621.6371×10-723.3246×10-94
F5(x)最优值-9.0084×103-1.1283×104-8.6938×103-1.2567×104-5.8572×103-6.1375×103-5.8370×103
均值-7.7866×103-9.5521×103-7.8359×103-9.9582×103-5.7150×103-5.2542×103-5.7102×103
标准差6.2266×1026.1018×1024.6312×1021.8645×1035.9340×1014.8384×1025.0417×101
F6(x)最优值8.9966×10-79.5861×10-78.8818×10-161.9619×10-173.3527×10-107.5495×10-140
均值2.0152×10-61.0313×10-54.7962×10-151.2669×10-163.0957×10-61.0427×10-138.7599×10-97
标准差3.2430×10-67.5707×10-61.9629×10-151.0643×10-166.1536×10-62.3982×10-144.7981×10-96
F7(x)最优值7.8676×10-32.0130×10-76.9557×10-771.2952×10-635.6843×10-1400
均值1.8783×10-19.1611×10-77.9884×10-772.1381×10-23.9688×1003.2376×10-1240
标准差1.2376×10-11.2528×10-65.7556×10-781.8596×10-24.5810×1001.4479×10-1230
F8(x)最优值1.3511×10-81.9957×1017.9048×10-147.9471×10-164.7788×10-91.0207×10-1540
均值2.6980×10-81.9960×1011.0321×10-135.3496×10-141.3191×10-51.0583×10-1525.3828×10-160
标准差6.7152×10-91.3281×10-31.3373×10-143.3632×10-123.6012×10-51.7850×10-1523.1932×10-29
F9(x)最优值7.2461×1005.8648×1002.7211×1004.5229×1006.6186×1004.9848×1001.0766×100
均值2.8344×1018.4747×1003.1967×1007.6200×1002.8141×1015.5731×1001.3383×100
标准差2.3867×1011.3962×1018.2469×1009.3611×1003.6313×1014.3085×1001.3731×100
F10(x)最优值1.2387×10-32.8781×10-26.6897×10-47.8048×10-44.0457×10-43.4319×10-43.0749×10-4
均值1.3042×10-31.7124×1012.2611×10-31.8059×10-39.7553×10-41.3896×10-23.0753×10-4
标准差4.6760×10-53.1307×1012.1081×10-38.7924×10-45.9784×10-42.5733×10-21.2408×10-7
F11(x)最优值5.4531×1005.0159×10-18.2930×10-11.3786×1013.3281×10-1-1.1432×100-3.8850×100
均值1.1156×1015.4681×1012.9638×1001.8963×1015.5429×10-14.7936×10-1-2.8346×100
标准差2.3658×1011.9526×1024.3358×1015.9687×1008.2673×1001.5948×1014.5165×10-1
F12(x)最优值-1.4302×100-1.0316×100-2.2453×100-2.7949×100-2.8326×100-3.2815×100-3.3220×100
均值-1.2440×100-1.0313×100-2.5724×100-2.5596×100-2.6211×100-3.0063×100-3.2269×100
标准差2.8702×10-13.2148×10-54.5623×1001.6497×10-14.3392×10-12.3553×10-15.3171×10-2

Fig.1

Convergence curves of each algorithm （partial）"

Fig.2

MSSA algorithms optimize coverage of sensor networks"

Fig.3

LOSSA algorithms optimize coverage of sensor networks"

Fig.4

CSSA algorithms optimize coverage of sensor networks"

Fig.5

GGSC-SSA algorithms optimize coverage of sensor networks"

Fig.6

SSA algorithms optimize coverage of sensor networks"

Table 4

Comparison of coverage results between the original algorithm and the improved algorithm"

算法资源充裕资源有限
SSA79.8565.84
MSSA93.4886.97
GGSC-SSA82.4971.32
CSSA83.6675.17
LOSSA86.5576.58
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