An Improved Shuffled Frog Leaping Algorithm

Shuffled Frog Leaping Algorithm (SFLA) is a new heuristic algorithm for global optimization. By analyzing the optimization mechanism of SFLA, an improved shuffled frog leaping algorithm is proposed. This new algorithm constructs the initial population using the principle of orthogonal design, which can make the population distribute more evenly in the feasible region and make the algorithm search more evenly in the feasible solution space. The sub-division method of the population is improved in order to narrow the individual difference and improve population diversity. In the solution update formula, an adaptive factor is designed to adjust the moving step, which speeds up the convergence process. Experimental results show that the improved SFLA avoids premature convergence effectively, improves the efficiency of search for complex functions and has a better convergence result and higher accuracy. Copyright © 2013 Binary Information Press.