The Research Of Shuffled Frog Leaping Algorithm Based On Local Orthogonal Crossover Operator And Its Application

Shuffled Frog Leaping Algorithm (SFLA) was first proposed by Eusuff and Lansey in 2003, which have received wide attention from foreign scholars. At present, this algorithm research has already improved many other application and has already become extremely active from research question in the interdisciplinary studies. Shuffled frog leaping algorithm is a new intelligent optimization method based on global cooperative search, which combines the advantages of Memetic algorithm and Particle Swarm Optimization algorithm and it is a new swarm intelligence algorithm after the presence of PSO .As a new optimization algorithm, SFLA has been proved to have many advantages, such as simple concept,less parameters,fast convergence speed,better global search capability and easy implementation, and so on. However, its theoretical foundation is weak, less research and application, and there is easy to converge to a local optimum, some functions in solving optimization problems result is not satisfactory and other defects.Since SFLA has so much shortcomings, this paper proposes a Shuffled Frog Leaping Algorithm based on local orthogonal crossover operator(SFLA-OCO),and analyzed the influence of the main parameters to the algorithm. The improved algorithm is applied to power system load forecasting problem and solving TSP, the main contents are as follows:1.A brief introduction of intelligent optimization problems and intelligent optimization algorithm was made. The basic principles, mathematical model and algorithm flow of SFLA were discussed in detail. The research progress of AFSA was summarized. All of the above indicated the importance of researching the SFLA.2.Aiming at the low optimization precision,slow convergence speed and a certain degree of blindness when update the individual worst values, this paper proposes a Shuffled Frog Leaping Algorithm based on local orthogonal crossover operator(SFLA-OCO), the value of parts of the new individual was improved. Therefore, the new algorithm not only improved the convergence speed,the optimization accuracy and the diversity of population but also balanced the global search ability and local search ability successfully.3.Through experiments, parameters selection was researched in details, which provided the useful reference for the further study of the SFLA.4.The Shuffled Frog Leaping Algorithm based on local orthogonal crossover operato(rSFLA-OCO), which was given from reference, was used to be a solution for parameters optimization of support vector machines. The SFLA-OCO-SVM predict model established by this paper was applied into short-term load forecasting in power systems, which received preferable result.5.The improved SFLA was applied into solving the TSP, which received preferable result. The results show that the improved algorithm can effectively solve the most combinatorial optimization problems and has not only good prospects but also a wide range of practical value.In conclusion, this paper makes a summarization of the prospect of the SFLA. However, since the theoretical analysis of the algorithm is not perfect, many questions are needed to be further researched.