基于量子狼群進(jìn)化的多目標(biāo)匯聚節(jié)點(diǎn)覆蓋算法
doi: 10.11999/JEIT160693 cstr: 32379.14.JEIT160693
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2.
(天津大學(xué)電氣自動化與信息工程學(xué)院 天津 300072) ②(天津市公安消防局 天津 300020)
基金項(xiàng)目:
國家自然科學(xué)基金(61571318)，青海省科技項(xiàng)目(2015-ZJ-904)，海南省科技項(xiàng)目(ZDYF2016153)
Multi-objective Sink Nodes Coverage Algorithm Based on Quantum Wolf Pack Evolution
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2.
(Electrical, Automation and Information Engineering College, Tianjin University, Tianjin 300072, China)
Funds:
The National Natural Science Foundation of China (61571318), The Qinghai Province Science and Technology Program (2015-ZJ-904), The Hainan Province Science and Technology Program (ZDYF2016153)
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摘要: 在構(gòu)建雙層無線傳感器網(wǎng)絡(luò)中，匯聚層覆蓋需要考慮無重復(fù)覆蓋面積、匯聚節(jié)點(diǎn)連通性和能耗平衡這3個關(guān)鍵問題。該文將上述3個問題統(tǒng)籌為多目標(biāo)優(yōu)化難題(MOP)，提出一種面向匯聚節(jié)點(diǎn)覆蓋的量子狼群進(jìn)化算法(QWPEA)，選擇出候選頭狼(CLW)群體，以滑模交叉、量子旋轉(zhuǎn)門、非門變異等方法產(chǎn)生尋優(yōu)高效的下一代量子編碼人工狼。仿真結(jié)果表明，該文所提算法能夠有效減少匯聚節(jié)點(diǎn)數(shù)，提高匯聚層結(jié)構(gòu)穩(wěn)定性，并平衡網(wǎng)絡(luò)能耗，適于大范圍，大規(guī)模傳感器節(jié)點(diǎn)網(wǎng)絡(luò)部署環(huán)境。在800 m800 m面積部署傳感器節(jié)點(diǎn)達(dá)到1000個時，匯聚有效覆蓋率較MOPSO, NSGA-II算法分別高29.55%和25.93%，匯聚通信能耗率分別高15.27%和18.63%，匯聚占通率分別低14.01%和15.46%。
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關(guān)鍵詞:
- 無線傳感器網(wǎng)絡(luò) /
- 量子狼群進(jìn)化算法 /
- 覆蓋 /
- 多目標(biāo) /
- 匯聚節(jié)點(diǎn)
Abstract: Satisfying non-repeated coverage, connectedness, and energy balance of sink layer are critical problems in multi-layers Wireless Sensor Networks (WSNs). They are overall planed as a Multi-objective Optimization Problem (MOP). For resolving it, the Quantum Wolf Pack Evolutionary Algorithm (QWPEA) is proposed, which actualizes the Candidate Leader Wolf (CLW) selection, sliding mode crossing, quantum rotating gate, and NOR gate mutation are used to obtain the more accurate wolfs location. Simulation results show that QWPEA can minus the number of sink nodes, promote the steadiness, and balance the energy consumption in a huge scale of WSNs effectively. While 1000 sensors are deployed on 800 m800 m with QWPEA, the sink effective coverage ratio is higher than either MOPSO as 29.55% or NSGA-II as 25.93%. And the sink communication energy consumption ratio is higher than the latter two methods as 15.27% and 18.63% separately. Also, the sink occupied ratio is lower than them as 14.01% and 15.46% severally. -
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