Nowadays how to find a globally optimized solution from a tremendously large search space has been one of the hottest area of computer science. One of the solution for this challenge is swarm intelligence optimization applications such as Ant colony algorithm. This kind of algorithms also called the bio-inspired optimization algorithm for these algorithms are inspired by the movements of animals and insects like firefly, cuckoos, bats and so on. Wolf Search Algorithm [4] as a young member of bio-inspired optimization algorithm was introduced by Simon Fong and Rui Tang in 2012. Wolf Search Algorithm (WSA) imitates the way wolves search for food and survive by avoiding their enemies. As one of these swarm intelligence optimization algorithms, WSA share the same contributes, the initialization can be very different form one function to another for the performance. Recently the self-adaptive algorithm has been a popular topic, and have been used into many well-known areas. So in here we introduces a self-adapting wolf search algorithm to match different situations in normal use. This development of WSA is inspired by the self-adaption part of the b at algorithm. The original self-adapted idea is from the self-adapting mechanism of self-adapted differential evolution known under the name jDE [2]. After implement the self-adaptive wolf search algorithm, the author will test it with the 8 common used optimization algorithm testing functions, and compare it with the original algorithm WSA to prove the better performance of SAWSA. At the end the author will use the SAWSA to solve a NP-hard real case problem the logistics planning problem, and then draw a conclusion to this whole work and try to find a future development of this work.