Improved Grey Wolf Optimization Algorithm based on SoftPlus Inertia Weight Strategy

Qixing Zhao

doi:10.62051/4g6y6057

Authors

Qixing Zhao

DOI:

https://doi.org/10.62051/4g6y6057

Keywords:

SoftPlus Function; Inertia Weight; Grey Wolf Optimization Algorithm; Optimization Accuracy; Rate of Convergence.

Abstract

In order to solve the problems of the Grey Wolf Optimization (GWO) algorithm, such as low optimization accuracy and low convergence speed, this paper proposes an Improved Grey Wolf Optimization Algorithm Based on SoftPlus Inertia Weight Strategy (SGWO). SGWO combines the nonlinear characteristics of SoftPlus function, and introduces the inertia weight strategy based on SoftPlus function to improve the optimization performance of GWO. Eight classic test functions are used to compare the performance of SGWO with five classical swarm intelligence algorithms. Experimental results show that SGWO is superior to other five-population intelligent algorithms in optimization accuracy and convergence speed.

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