I-GWO and Ex-GWO: improved algorithms of the Grey Wolf Optimizer to solve global optimization problems

In this paper, two novel meta-heuristic algorithms are introduced to solve global optimization problems inspired by the Grey Wolf Optimizer (GWO) algorithm. In the GWO algorithm, wolves are likely to be located in regions close to each other. Therefore, as they catch the hunt (approaching the solution), they may create an intensity in the same or certain regions. In this case, the mechanism to prevent the escape of the hunt may not work well. First, the proposed algorithm is the expanded model of the GWO algorithm that is called expanded Grey Wolf Optimizer. In this method, the same as GWO, alpha, beta, and delta play the role of the main three wolves. However, the next wolves select and update their positions according to the previous and the first three wolves in each iteration. Another proposed algorithm is based on the incremental model and is, therefore, called incremental Grey Wolf Optimizer. In this method, each wolf updates its own position based on all the wolves selected before it. There is the possibility of finding solutions (hunts) quicker than according to other algorithms in the same category. However, they may not always guarantee to find a good solution because of their act dependent on each other. Both algorithms focus on exploration and exploitation. In this paper, the proposed algorithms are simulated over 33 benchmark functions and the related results are compared with well-known optimization algorithms. The results of the proposed algorithms seem to be good solutions for various problems.

Automated summary

This paper introduces two novel meta-heuristic algorithms inspired by the Grey Wolf Optimizer (GWO) algorithm, which are the expanded Grey Wolf Optimizer and the incremental Grey Wolf Optimizer. Both algorithms focus on exploration and exploitation, and their simulated results over 33 benchmark functions show promising solutions for various problems.