Abstract:

The balance between global exploration and local development and the tuning of control parameters can be two main factors that extremely influence the performance of differential evolution (DE) algorithm. To solve these two problems, a self-adaptive DE algorithm with random neighborhood-based strategy and generalized opposition-based learning is proposed. Firstly, at each generation, the neighbors of the individuals from current population are selected at random, in which the finest one is selected as the base vector to execute the mutation operation, and the number of each individual in the neighborhood is dynamically updated with evolution process. In addition, a history-driven parameter self-adaptation method is implemented to adaptively update parameters during the evolution process of DE with the elite information. Finally, at the phase of initialization and the end of each generation, the generalized opposition-based learning strategy is applied to execute the initialization and generation jumping of population. Through three groups of simulation experiments based on 27 benchmark functions, the proposed algorithm is proved to have high searching accuracy, fast convergence speed and strong robustness.

Key words: differential evolution algorithm, random neighborhood, self-adaptation parameter, generalized opposition-based learning