Improved Grey Wolf Optimization Algorithm Based on Arctangent Inertia Weight
DOI:
https://doi.org/10.54097/hset.v56i.9821Keywords:
arctangent function, grey wolf optimization algorithm, convergence rate, swarm intelligence algorithm, inertia weightsAbstract
Grey Wolf Optimization (GWO) has several advantages in tackling optimization issues, but it still has some drawbacks such as slow convergence rate, low accuracy, and lack of stability. To address above drawbacks, this paper proposed an arctangent inertia weight strategy based on the arctangent function, and used the strategy to improve the GWO algorithm, thus proposing the Improved Grey Wolf Optimization Algorithm Based on Arctangent Inertia Weight (AGWO). Six classical test functions were selected to compare the convergence performance of AGWO with the other five classical swarm intelligence algorithms. The results show that AGWO has higher level of stability and computational accuracy, as well as faster convergence rate compared with the other five classical swarm intelligence algorithms.
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