International Journal of Optimization in Civil Engineering
عنوان نشریه
Iran University of Science & Technology
Engineering & Technology
http://ijoce.iust.ac.ir
18
agent2
2228-7558
doi
en
jalali
1401
1
1
gregorian
2022
4
1
12
2
online
1
fulltext
en
QUANTUM VERSION OF TEACHING-LEARNING-BASED OPTIMIZATION ALGORITHM FOR OPTIMAL DESIGN OF CYCLIC SYMMETRIC STRUCTURES SUBJECT TO FREQUENCY CONSTRAINTS
Optimal design
Optimal design
پژوهشي
Research
<span style="font-size:11.5pt"><span style="text-justify:inter-ideograph"><span style="text-autospace:none"><span style="font-family:"Times New Roman","serif""><span lang="IT" style="color:black">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. </span></span></span></span></span><br>
quantum-inspired evolutionary algorithm, teaching-learning-based optimization, population-based algorithm, circulant truss, quantum behaved particles, quantum teacher, frequency constraint.
245
278
http://ijoce.iust.ac.ir/browse.php?a_code=A-10-66-342&slc_lang=en&sid=1
A.
Kaveh
`180031947532846001847`

180031947532846001847
Yes
M.
Kamalinejad
`180031947532846001848`

180031947532846001848
No
K.
Biabani Hamedani
`180031947532846001849`

180031947532846001849
No
H.
Arzani
`180031947532846001850`

180031947532846001850
No