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...

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Parvaz Mahdabi, Saeed Jalili, Mahdi Abadi
• Keywords: Evolutionary Algorithms, GECCO 2008, Optimization, Quantum-inspired Evolutionary Algorithm, Quantuminspired Evolutionary Algorithm