PAIDDE: A Permutation-Archive Information Directed Differential Evolution Algorithm

Abstract

Evolutionary algorithms have shown great successes in various real-world applications ranging in molecule to astronomy. As a mainstream evolutionary algorithm, differential evolution (DE) possesses the characteristics of simple algorithmic structure, easy implement, and efficient search performance. Nevertheless, it still suffers from the issues of local optimal trapping and premature of evolution problems. In this study, we innovatively improve the performance of DE by incorporating a full utilization of information feedback, which includes the population's holistic information and the direction of differential vectors. The proposed permutation-archive information directed differential evolution (PAIDDE) algorithm is verified on a set of 29 benchmark numerical functions and 22 real-world optimization problems. Extensive experimental and statistical results show that PAIDDE can significantly outperform other 12 state-of-the-art algorithms in terms of solution qualities. Additionally, the computational complexity, solution distribution, convergence speed, search dynamics, and population diversity of PAIDDE are systematically analyzed.