An Improved Grey Wolf Optimization Algorithm with Variable Weights

Abstract EN

With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed.

And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed.

Simulation experiments are carried out, and comparisons are made.

Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA).

The novel VW-GWO algorithm is also verified in high-dimensional problems.