Leapfrog Algorithm And Application

With the rapid development of the computer science and technology, people's living space has been enlarged,and the field in which people recognize and change the world has been broaden. The demand for science and technology is in increase. Therefore high-performance optimizing technology and intelligence optimization is in urgent need. The shuffled frog leaping algorithm(SFLA) is a kind of rising swarm intelligence optimizer. It's concept is simple and it is easy to be implemented. After being presented by Eusuff and Lansey in 2003, It has been successfully applied to some fields. The shuffled frog leaping algorithm has a strong ability to achieve the most optimistic result. Meanwhile it has a disadvantage so far as its local minimum is concerned when used on some complicated combinatorial optimization problems. And the traditional model of SFLA is is applied to continous optimization problems,it is not fit for discrete combinatorial optimization problems. So based on the study of the mechanism of SFLA,discrete shuffled frog leaping algorithm(DSFLA) is presented ,and employs the simple neighborhood search to present three improvement strategies. The experimental has been done. The primary contents and results are following.First, based on the analysis of the SFLA's optimization mechanism, discrete shuffled frog algorithm for complex combinational optimization problems was put forward. The new algorithm adopts a new method of individual production to extend the traditional model of SFLA.Secondly, the swarm depends extremely on the local best result and the group best result. Based on this character, the improved algorithm was proposed employs the simple neighborhood search , and it can improve the convergence rate of DSFLA.Then, according to increasing the disturbance strategy can expand the search scope of local and global extreme result, the algorithm's convergence rate was further enhanced.Finally, Simulated annealing algorithm has a strong ablity to achieve the local optimistic result. And it can avoid local minimum. But on the other hand its ability to achieve the global optimistic result is quite weak. So DSFLA integrates the method of SA can further improve the algorithm, and reduce the probability of local minimums.Travelling salesman problem (TSP) and no idle flow shop problem(NIFS) which are constrained and discrete problems are discussed in the paper using DSFLA and three improvement strategies. The experimental results show that the proposed algorithm and its improvements are effective and efficient for different scale benchmarks problems.