Volume 12, Issue 2 (4-2022)
IJOCE 2022, 12(2): 245-278 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Kaveh A, Kamalinejad M, Biabani Hamedani K, Arzani H. QUANTUM VERSION OF TEACHING-LEARNING-BASED OPTIMIZATION ALGORITHM FOR OPTIMAL DESIGN OF CYCLIC SYMMETRIC STRUCTURES SUBJECT TO FREQUENCY CONSTRAINTS. IJOCE 2022; 12 (2) :245-278
URL: http://ijoce.iust.ac.ir/article-1-519-en.html
URL: http://ijoce.iust.ac.ir/article-1-519-en.html
Abstract: (8666 Views)
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.
Keywords: quantum-inspired evolutionary algorithm, teaching-learning-based optimization, population-based algorithm, circulant truss, quantum behaved particles, quantum teacher, frequency constraint.
Type of Study: Research |
Subject:
Optimal design
Received: 2022/04/10 | Accepted: 2022/04/12 | Published: 2022/04/12
Received: 2022/04/10 | Accepted: 2022/04/12 | Published: 2022/04/12
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
