Abstract
Shuffled frog leaping Algorithm (SFLA) is a new metaheuristic optimization algorithm with simple structure and fast convergence speed. This paper presents a modified shuffled frog leaping algorithm (MSFLA) based on a new searching strategy in which the frog adjusts its position according to the best individual within the memeplex and the global best of population simultaneously. Moreover, an effect factor was introduced to balance the global search ability and the local search ability in the strategy. Five benchmark functions were selected to compare the performance of MSFLA with SFLA. The simulation results show that the searching properties including convergence speed and the precision of MSFLA are obviously better than those of the original SFLA.
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