Neighborhood opposition-based differential evolution with Gaussian perturbation

Opposition-based learning (OBL) is an effective strategy to enhance many optimization methods among which opposition-based differential evolution (ODE) is one of the successful variants. However, ODE is a strict point-to-point algorithm, which may cause those opposite solutions to be ignored who are close to, however, have a gap to more promising solutions in the neighborhood. It usually provides a relatively narrow search channel for the candidate solutions and cannot maintain well population diversity. Hence, it is necessary to broaden the search neighborhood of the opposite solutions to increase the possibility of seeking out an even better solution. Thus, a new approach, GODE, is proposed to implement a Gaussian perturbation operation around the opposite point to expand its search neighborhood. Three different self-adaptive standard deviation models are then proposed and compared in the Gaussian perturbation strategy. Subsequently, a multi-stage perturbation strategy with different sized neighborhood is adopted to balance exploration and exploitation during different evolutionary stages. GODE is firstly compared with DE and ODE on CEC2014 benchmark suite with dimension of 30, 50 and 100. Many recent state-of-the-art algorithms using OBL strategy are further conducted comparison with GODE. The experimental results and statistical comparison analysis demonstrated that GODE has better or equal competitive performance against the classical and recent competitors.

Automated summary

The study proposes a new differential evolution algorithm GODE with Gaussian perturbation operation to expand the search neighborhood of opposite solutions. Three different self-adaptive standard deviation models and a multi-stage perturbation strategy are introduced to balance exploration and exploitation during different evolutionary stages. Experimental results show that GODE outperforms or performs equally to classical and recent competitors using opposition-based learning strategy.