Sciweavers

Share
GECCO
2008
Springer

A multi-start quantum-inspired evolutionary algorithm for solving combinatorial optimization problems

11 years 2 months ago
A multi-start quantum-inspired evolutionary algorithm for solving combinatorial optimization problems
Quantum-inspired evolutionary algorithms (QIEAs), as a subset of evolutionary computation, are based on the principles of quantum computing such as quantum bits and quantum superposition. In this paper, we propose a multi-start quantuminspired evolutionary algorithm, called MSQIEA. To improve the performance of the algorithm, a multi-measurement operator and a new strategy for updating the rotation angle is proposed. When Q-bit individuals start to converge to their final states, the best solution is stored and all Q-bits in each Q-bit individual are reinitialized. We compare the effectiveness of MSQIEA with a popular quantum-inspired evolutionary algorithm, called QEA, for solving 0-1 knapsack problem. The experimental results show that MSQIEA outperforms QEA and finds a solution with higher profit. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search General Terms Algorithms, Experimentation, Theory Keywords Evolutionary Al...
Parvaz Mahdabi, Saeed Jalili, Mahdi Abadi
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where GECCO
Authors Parvaz Mahdabi, Saeed Jalili, Mahdi Abadi
Comments (0)
books