Study On Multiple Water Supply Systems Based On Improved Hybrid Intelligent Algorithm

The rapid development of China's urbanization process,on the one hand,people's production and life have put forward higher requirements for water supply,On the other hand,lack of water resource in China,the uneven space-time distribution,water pollution and water resource utilization system did not get rid of vulgar development,a water supply system of the increasingly prominent contradiction between supply and demand in China.Single source water supply cannot meet the needs of urban water supply,and it is necessary to optimize the water supply system.This paper makes a theoretical study on multi-source water supply system,discusses the composition of multi-source water supply system and the objective,principle and method of optimizing the allocation of urban multi-water supply system are introduced.In building the city more optimal allocation model of water supply system,when choosing water system construction cost function related parameters,in order to "water supply and drainage design manual" to provide the relevant data as the foundation,using the MATLAB data processing platform,Power,Power function approximation model is adopted to improve the fitting.In addition,the optimal allocation model of urban multiwater supply system with the minimum annual cost is built with the operating cost of the water supply system.Hybrid leapfrog algorithm is an algorithm for performing memetics framework and USES the particle swarm optimization(pso)algorithm for local search mechanism of new artificial intelligence algorithm,has fast convergence speed,strong search ability,does not require the mathematical description convexity,etc.This article will introduce a hybrid leapfrog algorithm more water supply system optimization study,and the hybrid leapfrog algorithm was improved:(1)local search when introducing contraction factor,makes the worst frog evolution,at the same time to ethnic optimal frog and frog population optimal learning;(2)after the failure of the worst frog update strategy,the frog in the chaotic map was introduced to replace the worst frog randomly generated,and the blindness of calculation was reduced.In this paper,the performance of mixed frog jump algorithm is improved,and the comparison algorithm of detection is the traditional hybrid frog jump algorithm and genetic algorithm.In the three kinds of algorithm optimization respectively three kind of classic detection function,improve the precision of hybrid leapfrog algorithm improved,number of iterations is greatly reduced,and output accuracy is achieved faster when dealing with practical problems,and the solution accuracy has been improved.Finally,this paper takes the practical engineering project as an example and USES the improved hybrid frog jump algorithm to optimize the processing,and makes a full comparison with the traditional hybrid frog jump algorithm and the optimization result of genetic algorithm.It is concluded that the iteration times are less and the time is shorter,and the optimization result is ideal.