The Research And Application Of Shuffled Frog Leaping Algorithm Based On Average Value

Shuffled Frog Leaping Algorithm (SFLA) is presented by Lansey and Eusuff in2003as a kind of frog from the foraging behavior of swarm intelligence algorithms. It has a simple concept, fewer parameters, faster computing speed and global optimization ability, etc. The same to other swarm intelligence algorithm in optimization of some function problem, SFLA algorithm also exists some disadvantages, it is easy to fall into local optimum, the effect is not ideal.As Shuffled Frog Leaping Algorithm is easy to fall into local optimum, this paper proposes a Novel Shuffled Frog Leaping Algorithm Based on the Improved Average Value and briefly introduces the main parameters effect the performance of the algorithm, the improved algorithm is applied to the optimal scheduling and optimal combination of the problem of assembly line, this paper studies the main content as follows:1.In this paper, it describes optimization problem, intelligence optimization algorithms and several common optimization of intelligent algorithm,discusses the research background and significance of Shuffled Frog Leaping Algorithm and research situation, and briefly introduces the research content.2.The basic definition of basic Shuffled Frog Leaping Algorithm is briefly introduced,and the basic principle of basic Shuffled Frog Leaping Algorithm mathematical model and algorithm steps, the related parameters of the algorithm and the advantages and disadvantages are analyzed.3.As the Shuffled Frog Leaping Algorithm in partial update strategy is easy to limitations blindly, low precision and low optimal rate of convergence, the Shuffled Frog Leaping Algorithm is improved, and proposes a Novel Shuffled Frog Leaping Algorithm Based on the Improved Average Value, Basic leapfrog algorithm is introduced into the average in the subgroup of local search, and adopts adaptive probability directional random update each subgroup the optimal individual, better improved the update individual strategy, effective improved the diversity of population, making the global search ability and local search ability is improved further, can be better to improve the efficiency of the Shuffled Frog Leaping Algorithm and optimization precision.The effectiveness of the improved algorithm is verified by simulation experiments.4.Applying improved the Shuffled Frog Leaping Algorithm to optimize scheduling problem on assembly line and several intelligent optimization algorithm and other optimization scheduling to the comparative analysis, the results show that the Shuffled Frog Leaping Algorithm can get quickly and effectively optimal operation on an assembly line in the solution of the problem.5.With the improved of the Shuffled Frog Leaping Algorithm is applied to the optimal combination, with literature study and explore the instance of the power system, the analysis results show that the Shuffled Frog Leaping Algorithm compares with chaos optimization algorithm and genetic algorithm showing better effectiveness of the algorithm, and has good application prospect in the practical engineering optimization.Finally, the full text has carried on the brief summary to the Shuffled Frog Leaping Algorithm at the same time further the prospect of future research.