RT - Journal Article
T1 - QUANTUM VERSION OF TEACHING-LEARNING-BASED OPTIMIZATION ALGORITHM FOR OPTIMAL DESIGN OF CYCLIC SYMMETRIC STRUCTURES SUBJECT TO FREQUENCY CONSTRAINTS
JF - IUST
YR - 2022
JO - IUST
VO - 12
IS - 2
UR - http://ijoce.iust.ac.ir/article-1-519-en.html
SP - 245
EP - 278
K1 - quantum-inspired evolutionary algorithm
K1 - teaching-learning-based optimization
K1 - population-based algorithm
K1 - circulant truss
K1 - quantum behaved particles
K1 - quantum teacher
K1 - frequency constraint.
AB - As a novel strategy, Quantum-behaved particles use uncertainty law and a distinct formulation obtained from solving the time-independent Schrodinger differential equation in the delta-potential-well function to update the solution candidates’ positions. In this case, the local attractors as potential solutions between the best solution and the others are introduced to explore the solution space. Also, the difference between the average and another solution is established as a new step size. In the present paper, the quantum teacher phase is introduced to improve the performance of the current version of the teacher phase of the Teaching-Learning-Based Optimization algorithm (TLBO) by using the formulation obtained from solving the time-independent Schrodinger equation predicting the probable positions of optimal solutions. The results show that QTLBO, an acronym for the Quantum Teaching- Learning- Based Optimization, improves the stability and robustness of the TLBO by defining the quantum teacher phase. The two circulant space trusses with multiple frequency constraints are chosen to verify the quality and performance of QTLBO. Comparing the results obtained from the proposed algorithm with those of the standard version of the TLBO algorithm and other literature methods shows that QTLBO increases the chance of finding a better solution besides improving the statistical criteria compared to the current TLBO.
LA eng
UL http://ijoce.iust.ac.ir/article-1-519-en.html
M3
ER -