What is the “wolf” and “prey” in wolf search algorithm in Machine Learning?
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The “wolf” in the wolf search algorithm is a possible solution to a problem that the algorithm is trying to solve. Each wolf represents a point in the function’s domain. The prey is the best solution, or the global minimum of the function.
The algorithm works by simulating how wolves hunt for prey. There are four types of wolves in the algorithm: alpha, beta, delta and omega. The alpha wolf is the leader of the pack and has the current best solution among all wolves which follow its direction. The beta wolf is the second best solution and helps the alpha wolf and acts as another point of reference. The delta wolf is the third best solution and helps scout in a different direction for new prey providing a third source of reference. The omega wolf has the worst solution and just follows the other wolves although this is slightly random.
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