An Efficient Opposition Based Grey Wolf Optimizer for Weight Adaptation in Cooperative Spectrum Sensing

Kaur, Avneet; Sharma, Surbhi; Mishra, Amit

doi:10.1007/s11277-021-08129-4

Cited by 5 publications

(1 citation statement)

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“…Khan et al proposed using the social behavior of beetles to establish a computational model for operational management [ 4 ]. As one of the latest metaheuristic algorithms, grey wolf optimizer (GWO) is widely employed to settle real industrial issues because GWO maintains a balance between exploitation and exploration through dynamic parameters and has a strong ability to explore the rugged search space of the problem [ 5 , 6 ], such as the selection problem [ 7 , 8 , 9 ], privacy protection issue [ 10 ], adaptive weight problem [ 11 ], smart home scheduling problem [ 12 ], prediction problem [ 13 , 14 , 15 ], classification problem [ 16 ], and optimization problem [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning

Yin

2023

Biomimetics

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This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves β and δ with poor fitness in the position updating strategy; the second-best β wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results.

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