Application of discrete Grey Wolf Algorithm in balanced transport problem

Xiang, Ziquan; Yang, Jiaqi; Liang, Xueheng; Naseem, Muhammad Hamza

doi:10.1109/iaecst54258.2021.9695827

Cited by 2 publications

(1 citation statement)

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“…In this section, a set of experiments are performed with MATLAB 2016a to evaluate the performance of our proposed algorithm. By taking the NCT as the fitness value, we compare the performance of DAASO, IAPSO [9], and gray wolf optimizer (DGWO) [14] about the NCT in DSNs under different scenes, where the relevant parameters in IAPSO and DGWO adopt the values set in the original algorithm. The population size and the maximum number of iterations are set to 50 and 100, respectively.…”

Section: Resultsmentioning

confidence: 99%

Discrete Army Ant Search Optimizer-Based Target Coverage Enhancement in Directional Sensor Networks

et al. 2022

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Coverage of interest points is one of the most critical issues in directional sensor networks. However, considering the remote or inhospitable environment and the limitation of the perspective of directional sensors, it is easy to form perception blind after random deployment. The intension of our research is to deal with the bound-constrained optimization problem of maximizing the coverage of target points. A coverage enhancement strategy based on a discrete army ant search optimizer (DAASO) is proposed to solve the above problem, which is inspired by the biological habits of army ants. A set of experiments are conducted using different sensor parameters. Experimental results verify the effectiveness of the DAASO in coverage effect when compared to the existing methods.

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Section: Resultsmentioning

confidence: 99%