首页> 外文会议>Innovative Computing, Information and Control (ICICIC-2009), 2009 >Chaos Particle Swarm Optimization Algorithm for Estimating Solute Transport Parameters of Streams from Tracer Experiment Data
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Chaos Particle Swarm Optimization Algorithm for Estimating Solute Transport Parameters of Streams from Tracer Experiment Data

机译:从示踪实验数据估计流溶质输运参数的混沌粒子群优化算法

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In this paper, we combined chaotic search into standard particle swarm search into one and proposed a new algorithm named as chaos particle swarm optimization algorithm(CPSO). The CPSO algorithm may speed the search process, and improve the ability of seeking the global optimal solution and convergence. And the CPSO algorithm was applied to analysis of one-dimensional tracing test date of river stream with tracer injected instantaneously, and further to optimization of function for estimating the water quality parameters of river stream. The results show that the proposed algorithm is superior to the standard particle swarm optimization one in the speed of search and convergence.
机译:本文将混沌搜索与标准粒子群搜索相结合,提出了一种新的算法,称为混沌粒子群优化算法(CPSO)。 CPSO算法可以加快搜索过程,并提高寻找全局最优解和收敛性的能力。将CPSO算法应用于瞬时注入示踪剂的河流一维示踪试验数据的分析,并进一步优化了估算河流水质参数的功能。结果表明,该算法在搜索和收敛速度上均优于标准粒子群算法。

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