Grey prediction evolution algorithm for global optimization

•This article prosed a novel metaheuristic: grey prediction evolution algorithm based on the even grey model.•The new algorithm is identified by a novel technology use Grey Model as a reproduction operator to forecast offsprings.•The search mechanism of the proposed algorithm was theoretically analyzed according to Grey Prediction Theory.•The comparison experiments show that the proposed algorithm has more powerful ability of global optimization. This article uses the grey prediction theory to structure a new metaheuristic: grey prediction evolution algorithm based on the even grey model. The proposed algorithm considers the population series of evolutionary algorithms as a time series, and uses the even grey model as a reproduction operator to forecast the next population (without employing any mutation and crossover operators). It is theoretically proven that the reproduction operator based on the even grey model is adaptive. Additionally, the algorithmic search mechanism and its differences with other evolutionary algorithms are analyzed. The performance of the proposed algorithm is validated on CEC2005 benchmark functions and a test suite composed of six engineering constrained design problems. The comparison experiments show the effectiveness and superiority of the proposed algorithm. The proposed algorithm can be regarded as the first case of structuring metaheuristics by using the prediction theory. The novel algorithm is anticipated to influence two future works. The first is to propose more metaheuristics inspired by prediction theories (including some statistical algorithms). Another is that the theoretical results of these prediction systems can be used for this novel type of metaheuristics.