A Modified Shuffled Frog Leaping Algorithm Using Truncated Gaussian Distribution in Frog's Position Updating Process

摘要

The Shuffled Frog Leaping Algorithm (SFLA) is a population-based meta-heuristic algorithm which involves repeatedly updating the positions of frogs (solutions) in subgroup and shuffling frogs among subgroups to find the optimal solution. When updating a frog's position using the SFLA, the new position of a frog is equally likely to be at any point on a straight line between the current frog's position and the better frog's position. However, some parts of the line might be more beneficial to the global optimum solution exploration process. This paper investigates the use of a non-uniform distributed random number in updating frogs' positions, to explore how such a modification affects the performance of the convergence to a global optimum solution, when compared to the original SFLA's performance.