TY - CHAP
T1 - ST (Shafiabady-Teshnehlab) optimization algorithm
AU - Shafiabady, Niusha
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Appropriate local optimization algorithm
KW - Conventional optimization algorithms
KW - Interpolation and function approximation (numerical analysis)
KW - Iterative methods
KW - Local swarm intelligence algorithm
KW - Numerical analysis
KW - Optimisation
KW - Optimisation techniques
KW - Optimization task
KW - Particle swarm optimisation
KW - Shafiabady-teshnehlab optimization algorithm
KW - ST optimization algorithm
KW - ST optimization method
KW - Swarm intelligence
KW - Swarm optimization algorithms
UR - http://www.scopus.com/inward/record.url?scp=85118000610&partnerID=8YFLogxK
U2 - 10.1049/PBCE119G_ch4
DO - 10.1049/PBCE119G_ch4
M3 - Chapter
AN - SCOPUS:85118000610
SN - 9781785616297
VL - 2
SP - 83
EP - 110
BT - Swarm Intelligence - Volume 2
A2 - Tan, Ying
PB - Institution of Engineering and Technology
CY - London
ER -