The Improvement Of Shuffled Frog Leaping Algorithm And Its Application

Since shuffled frog leaping algorithm has been proposed,it has attracted wide attention from scholars and has been successfully applied in some engineering fields.SFLA in solving high dimensional problems the convergence rate is slow and easy to fall into local optimum,in order to improve the search performance of the SFLA,bases on its internal structure optimization analysis,puts forward several strategies.The improved SFLA is applied to the prediction of oxidation reduction potential(ORP)and job shop scheduling problem.This article main research work is as follows:1)In order to improve the convergence speed and avoid locating in local optimum when SFLA solves high dimensional problem,two improved algorithms are proposed in this paper ISFLA(improved shuffled frog leaping algorithm)based on local optimization strategy and TLBO-SFLA combined with teaching-learning-based optimization algorithm.In the local search of the ISFLA,the chaos optimization is introduced to the population initialization,expand the search scope of the initial population and diversity;the particle swarm is introduced to strengthen the exchange of information within the population;the opposition-based learning is introduced to increase the diversity of the algorithm to search for the late solution,reduce the probability of falling into local optimal.In the TLBO-SFLA algorithm,each sub population is seen as a class to learn,teachers enhance class level through the traditional "teaching" process,?learning? process between students can achieve differentiated learning.The results show that the proposed two algorithms not only have high convergence speed when solving high-dimensional problems,but also have better accuracy when solving complex problems such as multi-peaks.2)ORP as an important index of bacterial activity,the accurate prediction of ORP is conducive to the timely regulation of the key parameters of oxidation gold extraction process.In order to achieve the prediction of ORP,the support vector regression model is established to predict the ORP and the modified SFLA is used to optimize the key parameters of the prediction model to achieve high prediction accuracy.Select the actual production data of gold mine in Xinjiang province to establish prediction model,the results show that the support vector machine regression based on the modified SFLA has higher prediction accuracy.3)This can be used for the OSFLA(optimized shuffled frog leaping algorithm)algorithm to solve the job shop scheduling optimization,simulation test scheduling problem of selection criteria,the experimental results show that the OSFLA algorithm can not only find out the optimal solution,but also has faster search speed.