An improved linear prediction evolution algorithm based on nonlinear least square fitting model for optimization

Abstract The linear prediction evolution algorithm (LPE) is a recent addition in the field of optimization algorithm with few parameters and high exploration capability. LPE has shown excellent results in some real-world problems but still suffers from some issues such as slow convergence speed. To improve the performance of the LPE algorithm, this study presents an improved linear prediction evolution algorithm (ILPE) to enhance its exploration capability. The proposed ILPE algorithm treats the population series of evolutionary algorithms as a time series and uses the non-linear least square fitting model as a reproduction operator to forecast the next generation. The performance of the proposed ILPE is verified using the CEC2014 and CEC2017 benchmark functions. The comparison results show that ILPE outperforms LPE, and it is highly competitive with other state-of-the-art optimization algorithms.