A Novel Discrete Grey Wolf Optimizer for Solving the Bounded Knapsack Problem

Li, Zewen; He, Yichao; Li, Huanzhe; Li, Ya; Guo, Xiaohu

doi:10.1007/978-981-13-6473-0_10

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…They want to live in a pack, and the size of the single group from 5 to 12 almost has a strict social leading hierarchy [46]. The alpha wolf is also called the dominant wolf because it should be followed by the pack [47–48]. In the mathematical model of hunting, the mechanism of GWO is based on the following: tracking, chasing, moving towards prey; pursuing, encircling, and harassing the prey until he will move; and finally, attacking the prey. …”

Section: Proposed Methodologymentioning

confidence: 99%

Application of grey wolf optimisation algorithm in parameter calculation of overhead transmission line system

Shaikh

Chen

Jatoi³

et al. 2021

IET Science Measure & Tech

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The transmission line is the main component in the power system consisting of inductance, capacitance, and resistance. These parameters are important during the transmission line design. This research work applies a novel optimisation technique, grey wolf optimisation (GWO), to calculate the overhead transmission line parameter. The best optimal value is estimated with the control variables. Furthermore, the effect of different bundle conductors, that is, two, three, and four bundle conductors, radius, and spacing between the conductors on the transmission line is also analysed. GWO is a recently developed nature-inspired meta-heuristic algorithm. Single-phase and three-phase transmission line test systems have been adopted for testing purposes. The proposed algorithm is inspired by the command hierarchy and hunting system of grey wolves. The algorithm is applied to 14 benchmark optimisation functions with dimension and number of search agents. The results of the GWO algorithms are optimised and are superior as compared to previously applied algorithms. The proposed algorithm achieved the best optimal solutions for most of these functions that have been validated statistically. From the results, it is identified that the proposed algorithm is computationally efficient and performs significantly better in terms of accuracy, robustness, and convergence speed. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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