Chaos Particle Swarm Optimization Algorithm For Estimating Solute Transport Parameters In Streams From Tracer Experiment Data

Solute transport parameters are the basic data to establish models of river water quality and forecasting river water quality changes. At present, the solute transport parameters were get by using the corresponding analytical solution based on established methods of data analysis, analysis the river water mass tracer test data. So it will be very significant to estimate the solute transport parameters of river streams between environmental planning and environmental impact assessment of river drainage basin.In this paper, particle swarm optimization algorithm(PSO) and chaos particle swarm optimization algorithm(CPSO) have been proposed for estimating the solute transport parameters, aiming at the complicated problem of selecting best parameters in models of river water quality. By the numerical test of example, the present problems in these methods are analyzed and discussed. The main research task of this paper is as follows:1. The PSO algorithm is put into estimating solute transport parameters in streams from tracer experiment data. Application of the method to the published data shows the applicable and reliable of the proposed method. Also, the problems which have been improved by the PSO algorithm, is analyzed by numerical experiment.2.By use of the properties of ergodicity, stochastic property, and regularity of chaos , we embed chaotic search into standard particle swarm optimization, proposed a new chaos particle swarm optimization algorithm(CPSO) and improved the PSO algorithm.3.The CPSO algorithm is applied to identifying the aquifer parameters. Application of the method to the published data shows the applicable and reliable of the proposed method. The results of numerical experiment have also been analyzed .The CPSO is better than PSO in average optima, iteration numbers and successful rates form the analysis of numerical experiment.