Shuffled Frog Leaping Algorithm Improvement And Simulation Research For Optimization Of Control Parameters

Shuffled Frog Leaping Algorithm which proposed on the 2000-year, is a new kindof intelligent optimization algorithm. Its basic idea comes from the cultural geneticinheritance and its notable feature is a collaborative search strategy that is a mixture oflocal search and global information. Lots of simulation tests show that, Shuffled FrogLeaping Algorithm is a superlative and effective optimization technology when thefunction is high-dimension, sick, and more local optimum.The paper describes the principles of the algorithm, steps, and key parameters,verifies the algorithm by use of the typical characteristics of unconstrained testfunction, and compares advantageously with particle swarm algorithm and geneticalgorithms.Shuffled frog leaping algorithm, while having the advantages of easy tounderstand and fewer parameters, is not easy to jump out of local optimum, andconverges slowly. So three improved methods are proposed:Firstly, two improved algorithms based on the local search strategy are designedin the paper. One is using of the optimal crop individual and local population, bothaffect the worst frog genome individual at a certain proportion, the individualevolution formula is established and updates the worst individual location. The other,some poor fitness individuals are updated by use of independent evolution of localsearch. Two algorithms both improve against the single evolutionary of the localsearch part. The simulation results show that the two strategies improve the algorithmsolution speed. Certainly, the effectiveness has also been strengthened.Then, combined with the quantum algorithm and shuffled frog leaping algorithm,the quantum leapfrog algorithm is proposed. It uses the probability of the quantum bitto structure the frog individual, the revolving door changes quantum bit phase toupdate the individual, the quantum NOT gate to verify the optimal individual, andultimately achieve the optimization. It has a high convergence speed, a high solvingsuccess rate and good effectiveness.To verify the capability of the quantum leapfrog algorithm and the basic mixedleapfrog algorithm to optimize the control engineering problems, the paper use them to tuning PID controller parameter and optimize fuzzy controller parameters. Comparedwith particle swarm optimization and genetic algorithms simulation results, thequantum leapfrog algorithm has satisfactory performance in control engineering field.