This thesis is devoted to three intelligent optimization algorithms: Particle Swarm Optimization (PSO) algorithm, Shuffled Frog Leaping Algorithm (SFLA) and Harmony Search (HS) algorithm.

The projection pursuit model is an effective classification approach,this paper attempts to use shuffled frog leaping algorithm for optimizing the projection pursuit model.

Due to limitations of traditional traffic interval programming methods,a novel traffic interval programming method (SFLA-FCM) is proposed based on Shuffled Frog Leaping Algorithm(SFLA) and Fuzzy C Means(FCM).

Basic shuffled frog leaping algorithm(SFLA) easily trapped into local optima and had a slow convergence speed when it was used to address complex functions,in order to overcome the shortcomings,this paper proposed an improved SFLA.

Meme triangular probability distribution shuffled frog-leaping algorithm

Modified shuffled frog-leaping algorithm to solve traveling salesman problem

To improve further performance of multi-user detection based on Discrete Shuffled Frog Leaping Algorithm(DSFLA),a novel hybrid algorithm that employs DSFLA and Hopfield Neural Network(HNN) for the multi-user detection is presented.

In order to search in discrete search space,a leap-frog algorithm named DSSLFA is proposed.

Research of location based on mixed algorithm of weighted centroid and DV-Hop in WSN

This paper presents a fixed point number and floating point number mixed algorithm for the precise straightening press automatic detecting system.

Numerical experiments show the mixed algorithm corrects the BFGS lo.