本文基于费用函数最小化方法，提出一种混合并行量子进化算法用于文本图像的边缘检测。

In this paper we present a hybrid parallel quantum evolutionary algorithm (PQEA) based on cost minimization technique for edge detection.

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将量子群进化算法(QEA)与蚁群系统(acs)进行融合，提出一种新的量子蚁群算法(QACA)。

The algorithm is based on the combination of quantum evolutionary algorithm (QEA) and ant colony system (ACS), a new algorithm, quantum ant colony algorithm (QACA) is proposed.

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此外，量子进化算法具有收敛快和好的全局搜索特性，因此它比传统的进化算法更适于并行结构的实现。

QEA is more suitable for parallel structure than the conventional evolutionary algorithms because of rapid convergence and good global search capability.

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量子衍生进化算法是基于量子计算原理的一种进化算法。

Quantum Inspired Evolutionary Algorithm (QEA) is a type of evolutionary algorithm based on principles of Quantum Computing (QC).

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通过直接将量子位的Bloch坐标视为基因位，提出一种基于量子位Bloch坐标的量子衍生进化算法。

By directly regarding the Bloch coordinates of qubit as genes in chromosome, a quantum-inspired evolution algorithm is proposed.

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通过直接将量子位的Bloch坐标视为基因位，提出一种基于量子位Bloch坐标的量子衍生进化算法。

By directly regarding the Bloch coordinates of qubit as genes in chromosome, a quantum-inspired evolution algorithm is proposed.

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