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Chaotic Particle Swarm Optimization Algorithm with Niche and Its Application in Cascade Hydropower Reservoirs Operation

机译:利基的混沌粒子群优化算法及其在级联水电站运行中的应用

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Niche evolutionary strategy and chaotic searching were introduced into PSO, called as Chaotic Particle Swarm Optimization Algorithm with Niche (CNPSO) in this thesis. Restricted competition selection method was used to establish niche, in which each species excluded each other and dynamically formed their own searching spaces, effectively maintain the diversity of the species, so as to avoid local convergence. The chaotic searching further improved the global optimization searching precision. CNPSO was programmed to do the optimization regulation of 14 cascade hydropower stations with giant reservoirs by 48-year run-off series. With the result showing that CNPSO is highly efficient in optimization searching, capable of solving the complicated multi dimensional, strong-constraint, multi-states, multi-stages and non-linear problems such as optimization regulation of cascade hydropower stations with giant reservoirs.
机译:将利基进化策略和混沌搜索引入PSO,称为本论文中的Chaotic粒子群优化算法,叫做Niche(CNPSO)。限制竞争选择方法用于建立利基,其中每个物种彼此排除并动态形成自己的搜索空间,有效地保持物种的多样性,以避免局部会聚。混沌搜索进一步提高了全局优化搜索精度。 CNPSO被编程为用48年径流系列用巨型水库进行14家级联水电站的优化调节。结果表明,CNPSO在优化搜索方面具有高效,能够解决具有巨型储层的复杂的多维,强制,多态,多阶段和非线性问题,例如级联水电站的优化调节。

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