The Improved Shuffled Frog Leaping Algorithm

Abstract

Shuffled frog leaping algorithm is a memetic metaheuristic and population based intelligent inquiry metaphor im-pacted by normal memetics. Predominantly SFLA has been utilized for arrangement of combinative streamlining issues. In SFL algorithm there are two basic things one is population which is divided into several memeplexes and another is information between these memeplexes has been exchanged. In Shuffled frog leaping algorithm one issue is available which is slow convergence. To resolve this problem Improved shuffled frog leaping algorithm is proposed. In both phase local best and global best phase (1- it/Maxit) term is multiplied to get better solution. The proposed strategy is tested more than 12 benchmark functions and com- pared with different algorithms like basic shuffled frog leaping algorithm (SFLA), gravitational search algorithm (GSA), spider monkey ptimization (SMO) and differential evolution (DE).