A Differential Evolution Based on Individual-Sorting and Individual-Sampling Strategies

Differential Evolution has been a simple and efficient heuristic for global optimization over continuous spaces due to its remarkable performance. In this paper, we firstly modified the traditional structure of population in Differential Evolution and proposed a new strategy for population setting, in which a population was sorted based on the fitness values of individuals. Another new method was saltatory sampling with a nonrandom order, which was utilized to select candidates for the mutation operation. Furthermore, the strategy of survival of the fittest was used for individual selection operation. Then we propose the Differential Evolution based on Individual-Sorting and Individual-Sampling (ISSDE), of which control parameters was experimentally set. The proposed algorithm is tested on benchmark functions and is compared with traditional Differential Evolution. The simulation results show that the proposed ISSDE has a better performance both in convergence speed and robustness.