Core Concepts
AE-QTS algorithm enhances quantum-inspired tabu search, outperforming QTS by 20-30% in solving knapsack problems.
Abstract
I. Introduction to Metaheuristics and Quantum Algorithms:
Metaheuristics solve complex problems like NP-complete issues.
Quantum algorithms have made significant advancements.
II. Contributions of the Study:
AE-QTS improves efficiency by 20%, maintaining simplicity.
III. Principles of Quantum Computing:
Qubits are wave-like with corresponding amplitudes.
IV. Explanation of 0/1 Knapsack Problem:
A classic combinatorial optimization problem with profit and weight constraints.
V. Amplitude-Ensemble QTS (AE-QTS):
AE-QTS incorporates population information into qubits for efficient solution finding.
VI. Experiments and Results:
AE-QTS shows superior convergence results compared to other algorithms.
VII. Conclusion:
AE-QTS enhances QTS performance without increasing complexity.
Stats
"Experimental results show that the AE-QTS outperforms other algorithms, including the QTS, by at least an average of 20% in all cases and even by 30% in some cases."
"AE-QTS has a convergence speed close to DE at the beginning, and the convergence results are as good as QTS in the later stage."
"The 'amplitude-ensemble' mechanism can increase the performance of QTS by approximately 34.74%, 30.99%, and 20.62% for problems with 100, 250, and 500 items."
Quotes
"Our method has better search performance."
"AE-QTS is better than QTS and is even the best algorithm in comparison."
"AE-QTS has a convergence speed close to DE at the beginning."