This paper presents a multi-objective quantum Particle Swarm Optimization(PSO) based on game theory.
提出一种基于博弈论的多目标量子粒子群算法。
This paper puts forward the design method based on Quantum Particle Swarm optimization for optimization design of four bar linkage of multi-objective, multi-constraint conditions.
对于多目标、多约束条件的四连杆机构优化设计，本文提出了一种基于量子粒子群算法求解的设计方法。
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。
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。
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。
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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