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An Improved Quantum Evolutionary Algorithm for High Dimension Knapsack Problem

机译:一种改进的高尺寸背包问题的量子进化算法

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Knapsack problem is a typical combinatorial optimization problem, which has attracted wide attention because of its simple description, difficult solution and wide application background. For the knapsack problem, we proposed an improved quantum evolutionary algorithm. In this algorithm, the qubits are initialized according to the value densities of their corresponding items, the angle of Q-gate is determinated through direct comparison between the quantum chromosome and the optimal solution, random crossover method is taken to share information among chromosomes, and Hε gate is used to prevent from premature convergence. Finally, experiment demonstrates the effectiveness of the algorithm.
机译:背包问题是一种典型的组合优化问题,由于其简单的描述,困难的解决方案和广泛的应用背景,引起了广泛的关注。对于背包问题,我们提出了一种改进的量子进化算法。在该算法中,根据其相应项目的值密度初始化Qubits,通过量子染色体和最佳解决方案之间的直接比较来确定Q栅的角度,随机交叉方法被采用染色体之间的信息分享信息。 Hε门用于防止过早收敛。最后,实验表明了算法的有效性。

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