Modification Shuffled Frog Leaping Algorithm (SFLA) Applied with the Single-Objective Function

This proposed algorithm presents the modification of the Shuffled Frog Leaping Algorithm (SFLA) applied with single-objective optimization functions. In the original SFLA, the concept of the moving position of the frogs searching for a better solution is uniformly random. The SFLA constantly updates the positions of frog solutions in memeplexes and shuffles frogs into subgroups to find the optimal solution. In this research, we modify the SFLA by including the angle and sigma in the moving process. The reason is to increase the search space when the frogs move into a straight line. The method is studied in modification SFLA, how the algorithm works when a single-objective function concept. The experiment chooses the two benchmark functions to test the optimization of the proposed method. The Ackley and the De Jong F5 function test the single-objective optimization when the algorithm converts to the optimum. The experimental shown in the graph with the time domain and accuracy domain when the convergence rate of the proposed method is to the optimum solution. The comparison graph is shown in the primary SFLA, the modification SFLA in the convergence rate of the optimum solution in each function.