A Clonal Selection Based Shuffled Frog Leaping Algorithm

Abstract

The shuffled frog leaping algorithm (SFLA) is a recent meta-heuristic memetic algorithm used for optimization having a simple algorithm with a fast calculation time. It mimics the social behavior of a species (frogs) found in nature. Clonal selection algorithm (CSA) is an optimization algorithm developed based on the processes occurring in natural immune system. In this paper, a novel algorithm is proposed that is based on a modified CSA and SFLA. In the proposed algorithm a modified CSA is used for the best candidates in the population to progress and SFLA for the worst candidates in the population to move towards the best candidates. The power of the algorithm lies in the fact that it avoids stagnation and has a very fast convergence speed. The algorithm is tested against SFLA for five functions in which it greatly outperforms SFLA in terms of convergence rate and the optimum result obtained.