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

Authors:
Parvaz Mahdabi;Saeed Jalili;Mahdi Abadi
Affiliations:
Tarbiat Modares University, Tehran, Iran;Tarbiat Modares University, Tehran, Iran;Tarbiat Modares University, Tehran, Iran
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 3
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Abstract

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 quantum-inspired 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.