Improved Ant Colony Optimization Algorithm for Optimized Nodes Deployment of HAP-Based Marine Monitoring Sensor Networks

Duan, Jianli; Liu, Yuxiang; Lin, Bin; Jiang, Yuan; Hou, Fen; Li, Wan–Tong

doi:10.1007/978-981-13-6508-9_113

Cited by 3 publications

(2 citation statements)

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“…Compare the size of A, compare the probability factor value with 0.5, and select the corresponding location update formula. Update the current position according to the spiral mechanism of equation (16).…”

Section: Application Of Sca-woa Algorithm In Optimal Coverage Of Hwsnsmentioning

confidence: 99%

“…This method can effectively avoid the algorithm from falling into premature and accelerate the convergence of the algorithm, but there are insufficient considerations for the random deployment of nodes, the perception blind zone, and the overlap zone. Duan et al 16 proposed to apply the improved ant colony algorithm to network node coverage optimization. Although it increases the algorithm’s strong local search ability, it does not consider actual environmental factors to a certain extent, which affects the real-time performance of network coverage optimization.…”

Section: Related Workmentioning

confidence: 99%

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Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

Yue

You

Wang

et al. 2021

International Journal of Distributed Sensor Networks

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Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine–cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

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Section: Application Of Sca-woa Algorithm In Optimal Coverage Of Hwsnsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%