An in-depth analysis of the Gray Wolf Optimization Algorithm (GWO) and its strengths and weaknesses

The Gray Wolf Optimization Algorithm (GWO) is a population-based metaheuristic algorithm that simulates the leadership hierarchy and hunting mechanism of gray wolves in nature.

Inspiration of Gray Wolf Algorithm

1. Gray wolves are considered to be the top predators and are at the top of the food chain.

2. Gray wolves like to live in groups (living in groups), with an average of 5-12 wolves in each pack.

3. Gray wolves have a very strict social dominance hierarchy, as shown below:

Alpha wolf:

## The #Alpha wolf occupies a dominant position in the entire gray wolf pack and has the right to command the entire gray wolf pack.

In the application of algorithms, Alpha Wolf is one of the best solutions, the optimal solution produced by the optimization algorithm.

Beta Wolf:

The Beta wolf reports to the Alpha wolf regularly and helps the Alpha wolf make the best decisions.

In algorithmic applications, Beta wolves can be called the suboptimal solution among all possible solutions to a problem. If some solutions are not suitable for the best optimal solution, then that solution will be adopted.

Delta Wolf:

Delta Wolf is subordinate to Beta Wolf, provides continuous updates for Alpha and Beta Wolf, and is the superior of Omega Wolf.

In the application of algorithms, Delta Wolf can be called the third best solution among all possible solutions to the problem. But for all possible solutions, the third best solution is evaluated based on the best and second fit solutions.

Omega Wolf:

The Omega wolf is responsible for hunting and taking care of the young wolves.

In algorithm applications, Omega Wolf can be called the optimal solution generated by all possible solutions, and the optimal solution is evaluated only by the third optimal solution, which will not be compared with the best solution. solutions for comparison.

Gray wolves follow a special hunting technique where the entire pack hunts its prey in packs. The chosen prey is separated from the pack by the Omega wolf, and the chosen prey is chased and attacked by the Delta wolf and Beta wolf. The Gray Wolf algorithm is optimized according to this law and produces optimal solutions by using various built-in functions.

Gray Wolf Algorithm Logic

The Gray Wolf Optimization Algorithm (GWO) usually reduces the data operation time. The algorithm will decompose the entire complex problem into multiple subsets and provide each subset with An agent, similar to the overall hierarchy of a gray wolf pack, outputs all solutions and ranks them to produce the best optimal solution.

Therefore, the Gray Wolf Optimization Algorithm (GWO) must produce the best solution by repeatedly implementing the task. Once the best-fitting solution is determined, the algorithm stops iterating.

However, the optimal solution is not absolute. In rare cases, the gray wolf algorithm will choose to output a suboptimal solution to the problem.

Advantages and Disadvantages of Gray Wolf Algorithm

Advantages: Compared with other optimization algorithms, the optimization process of Gray Wolf algorithm is faster because they first obtain the answer, then compare different answers and respond accordingly Sort them to output the best solution.

Disadvantages: Gray Wolf optimization algorithm is a heuristic optimization algorithm, and the optimal solution generated is only close to the original optimal solution, and is not the real optimal solution to the problem.

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