ST (Shafiabady-Teshnehlab) optimization algorithm

Shafiabady-Teshnehlab (ST) optimization algorithm is a local swarm intelligence algorithm that has been inspired from the motion of the molecules in the air. Similar to all the other swarm optimization algorithms, the mentioned algorithm uses iterative approach by updating the values of the cells in each particle. This method is superior to conventional optimization algorithms because of its capability in finding the local minimum in very few and incomparably less numbers of iterations relative to other local optimization methods; hence, ST optimization algorithm leads to faster decisionmaking speed. The other specification of this algorithm is the precision and accuracy of the results in comparison with the algorithms in its own group. In addition, this algorithm has the ability to perform the optimization task accurately when dealing with several unknown values simultaneously; hence, increasing the dimensions of the search space does not deteriorate the optimization results like the other conventional algorithms. The only shortcoming of ST optimization algorithm is its local nature that makes it sensitive to the initial values that represent the particles in the search space. The various advantages of ST optimization method make it an appropriate local optimization algorithm.