An Improved Cuckoo Search Algorithm Using Elite Opposition-Based Learning and Golden Sine Operator

Li, Pengcheng; Zhang, Xuanyu; Zain, Azlan Mohd; Zhou, Kailiang

doi:10.1007/978-3-031-06794-5_23

Cited by 1 publication

(1 citation statement)

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“…Due to the results, Li et al's proposed improved cuckoo search method significantly increases the algorithm's convergence accuracy and speed. It is based on elite OBL and the golden sine operator 14 . By making these changes, the conventional CS's shortcomings were improved and its ability to solve optimization issues will be enhanced, and all of them have achieved good results.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks

Yang,

Xiang,

Kang

et al. 2024

Baghdad Sci.J

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The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting regional monitoring into point monitoring utilizing the discretization method in WSN. In the experiments, the ICS-PSO-OBL with the standard CS and three CS variants (MACS, ICS-2, and ICS) are utilized to execute the simulation experiment under different numbers of nodes (20 and 30, respectively). The experimental results reveal that the optimized coverage of ICS-PSO-OBL is 18.36%, 7.894%, 15%, and 9.02% higher than that of standard CS, MACS, ICS-2, and ICS when the number of nodes is 20. Moreover, it is 16.94%, 9.61%, 12.27%, and 7.75% higher when the quantity of nodes is 30, the convergence speed of ICS-PSO-OBL, and the distribution of nodes is superior to others.

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Section: Introductionmentioning

confidence: 99%