Surrogate-assisted Phasmatodea population evolution algorithm applied to wireless sensor networks

Liang, Lulu; Chu, Shu-Chuan; Du, Zhi-Gang; Pan, Jeng‐Shyang

doi:10.1007/s11276-022-03168-6

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…Currently, the main sensing models of sensor nodes are binary and probabilistic sensing models. The binary sensing model is an ideal sensing model that ignores the attenuation of the signal during transmission, i.e., the ability to transmit the signal is constant [49]. The probabilistic sensing model takes into account the fading of the nodes during the message of the signal, i.e., the ability to transmit the signal decreases with increasing distance.…”

Section: Wireless Sensor Networkmentioning

confidence: 99%

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Surrogate-assisted sine Phasmatodea population evolution algorithm applied to 3D coverage of mobile nodes

Chu,

Liang,

Pan

et al. 2024

Complex Intell. Syst.

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Deploying static wireless sensor nodes is prone to network coverage gaps, resulting in poor network coverage. In this paper, an attempt is made to improve the network coverage by moving the locations of the nodes. A surrogate-assisted sine Phasmatodea population evolution algorithm (SASPPE) is used to evaluate the network coverage. A $$50 \times 50$$ 50 × 50 hill simulation environment was tested for the number of nodes of 30 and 40 and radii of 3, 5 and 7, respectively. The results show that the SASPPE algorithm has the highest coverage, which can be up to 23.624% higher than the PPE algorithm, and up to 5.196% higher than the PPE algorithm, ceteris paribus. The SASPPE algorithm mixes the GSAM with LSAMs, which balances the computational cost of the algorithm and the algorithm’s ability to find optimal results. The use of hierarchical clustering enhances the stable type of the LSAMs. In addition, LSAMs are easy to fall into local optimality when they are modeled with local data, and the use of sine Phasmatodea population evolution algorithm (Sine-PPE) for searching in LSAMs alleviates the time for the algorithm to fall into local optimality. On 30D, 50D, and 100D, the proposed algorithm was tested by 7 test functions. The results show that the algorithm has significant advantages on most functions.

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Section: Wireless Sensor Networkmentioning

confidence: 99%

“…Liang [49] proposed a two-level substitution-assisted PPE algorithm (SAPPE), which improves the PPE algorithm using two surrogate models constructed by RBFN. The algorithm was applied to the 3D coverage problem of wireless sensors and achieved good results.…”

Section: Introductionmentioning

confidence: 99%