Memetic frog leaping algorithm for global optimization

Abstract

Developing an effective memetic algorithm that integrates leaning units and achieves the synergistic coordination between exploration and exploitation is a difficult task. In this paper, we propose a memetic algorithm based on the shuffled frog leaping algorithm, which is fulfilled by three units: memetic diffusion component, memetic evolutionary component and memetic learning component. Memetic diffusion component enhances the diversity of population by the shuffled process. Memetic evolutionary component accomplishes the exploitation task by integrating the frog leaping rule, geometric center, Newton’s gravitational force-based gravitational center and Lévy flight operator. Memetic learning component improves the exploration by an adaptive learning rule based on the individual selection and the dimension selection. In order to evaluate the effectiveness of the proposed algorithm, 30 benchmark functions and a real-world optimization problem are used to compare our algorithm against 13 well-known heuristic methods. The experimental results demonstrate that the performance of our algorithm is better than others for the continuous optimization problems.