Inspired by the idea of hybrid optimization algorithms, this paper proposes two hybrid Quantum Evolutionary algorithms (QEA) based on combining QEA with Particle Swarm optimization (PSO).

文章将量子进化算法(QEA)和粒子群算法(PSO)互相结合，提出了两种混合量子进化算法。

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With bi-partitioning strategy, by maximizing the module density, an algorithm is proposed based on discrete quantum particle swarm optimization for complex network community detection.

采用二分策略，通过最大化模块密度，提出了基于离散量子粒子群优化进行复杂网络社区检测的算法。

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According to still existing problem of Quantum-behaved Particle Swarm Optimization (QPSO), a new QPSO with two Particle Swarms based on public history researching side-by-side (TPHQPSO) is presented.

针对量子粒子群算法存在的问题，设计基于公共历史的两种群并行搜索的量子粒子群算法。

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According to still existing problem of Quantum-behaved Particle Swarm Optimization (QPSO), a new QPSO with two Particle Swarms based on public history researching side-by-side (TPHQPSO) is presented.

针对量子粒子群算法存在的问题，设计基于公共历史的两种群并行搜索的量子粒子群算法。

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