A Parallel Hybridization of Clonal Selection with Shuffled Frog Leaping Algorithm for Solving Global Optimization Problems (P-AISFLA)

Abstract

Shuffled frog leaping Algorithm (SFLA) is a new memetic, local search, population based, Parameter free, meta-heuristic algorithm that has emerged as one of the fast and robust algorithm with efficient global search capability. SFLA has the advantage of social behavior through the process of shuffling and leaping that helps for the infection of ideas. Clonal Selection Algorithms (CSA) are computational paradigms that belong to the computational intelligence family and is inspired by the biological immune system of the human body. CSA has the advantages of Innate and Adaptive Immunity mechanisms to antigenic stimulus that helps the cells to grow its population by the process of cloning whenever required. A hybrid algorithm is developed by utilizing the benefits of both social and immune mechanisms. This hybrid algorithm performs the parallel computation of social behavior based SFLA and Immune behavior based CSA to improve the ability to reach the global optimal solution with a faster and a rapid convergence rate. This paper compared the Conventional CLONALG and SFLA approaches with the proposed hybrid algorithm and tested on several standard benchmark functions. Experimental results show that the proposed hybrid approach significantly outperforms the existing CLONALG and SFLA approaches in terms of Mean optimal Solution, Success rate, Convergence Speed and Solution stability.